Protein and lipid biomarkers providing consistent improvement to the prediction of type 2 diabetes

ABSTRACT

The invention relates to biomarkers associated with Diabetes, including protein and lipid metabolite biomarkers, methods of using the biomarkers to determine the risk that an individual will develop Diabetes, and methods of screening a population to identify persons at risk for developing Diabetes and other pre-diabetic conditions.

PRIORITY CLAIM

This application is a continuation of U.S. application Ser. No.14/945,200 filed Nov. 18, 2015, which is a continuation of U.S.application Ser. No. 13/504,720, now U.S. Pat. No. 9,217,747, filed Sep.17, 2012, which is a national stage entry of PCT/US10/54398, filed Oct.28, 2010, which claims priority to U.S. Provisional Patent ApplicationSer. No. 61/256,302, filed Oct. 29, 2009, the contents of each of whichare incorporated herein by reference.

TECHNICAL FIELD

The invention relates to biomarkers associated with Diabetes, methods ofusing the biomarkers to determine the risk that an individual willdevelop Diabetes, and methods of screening a population to identifypersons at risk for developing Diabetes and other diabetic conditions.

BACKGROUND

Fifteen million people in the United States have Type 2 diabetes. Inboth human and economic terms, diabetes is one of the most costlydiseases in the nation today. The cost of medical care and services totreat diabetes is estimated to have been $91.8 billion in 2002. Another$40.2 billion of lost productivity, disability and premature death isalso attributable to the disease. One million new cases are diagnosedeach year, and many people do not learn they have the disease until theydevelop one of its life-threatening complications, which include heartdisease, stroke and kidney disease.

Diabetes has been attributed to both genetic and lifestyle factors,including obesity, age, sedentary lifestyle, hypertension, and use ofdrugs that block insulin action or antagonize insulin action. As aresult, in the absence of a predictive diagnostic, single factors cannot reliably be used to accurately assess an individual's propensity fordeveloping the disease. Type 2 diabetes is typically diagnosed bymeasuring fasted plasma glucose, 2-hour plasma glucose or random plasmaglucose (if symptoms are present). Persons with early-stage Type 2diabetes are usually asymptomatic and may not realize they are ill; theymay live for many years with uncontrolled diabetes before symptoms everoccur. When they do occur, those symptoms are often related to alife-threatening complication. Treatment or lifestyle changes in theearly stages of disease can delay and possibly even prevent developmentof diabetes and its related complications.

Treatment for prediabetes can slow or reverse the disease in someindividuals, particularly in early stage disease. Lifestyle interventionor treatment with, for instance, metformin in persons at high risk canreduce the incidence of diabetes by 58% and 31% respectively. Hence, asimple to administer method to monitor early stage disease progression,and determine efficacy of treatment, would greatly improve diseasetreatment and outcomes.

Type 2 Diabetes (non-insulin-dependent Diabetes or adult-onset Diabetes)results from insensitivity to insulin, and accounts for 90% of Diabetesworldwide. Gestational Diabetes is a loss of blood sugar control(hyperglycemia) that occurs during pregnancy. Type 2 Diabetes ischaracterized by disorders of insulin action and insulin secretion,either of which may be the predominant feature. Type 2 Diabetes patientsare characterized with a relative, rather than absolute, insulindeficiency and are insulin resistant. At least initially, and oftenthroughout their lifetime, these individuals do not need supplementalinsulin treatment to survive. Type 2 Diabetes accounts for 90-95% of allcases of Diabetes and can go undiagnosed for many years because thehyperglycemia is often not severe enough to provoke noticeable symptomsof Diabetes or symptoms are simply not recognized. The majority ofpatients with Type 2 Diabetes are obese, and obesity itself may cause oraggravate insulin resistance. Many of those who are not obese bytraditional weight criteria may have an increased percentage of body fatdistributed predominantly in the abdominal region (visceral fat).Whereas patients with this form of Diabetes may have insulin levels thatappear normal or elevated, the high blood glucose levels in thesediabetic patients would be expected to result in even higher insulinvalues had their beta cell function been normal. Thus, insulin secretionis often defective and insufficient to compensate for the insulinresistance. On the other hand, some hyperglycemic individuals haveessentially normal insulin action, but markedly impaired insulinsecretion.

Pre-diabetics often have fasting glucose levels between normal and frankdiabetic levels. Abnormal glucose tolerance, or “impaired glucosetolerance” can be an indication that an individual is on the path towardDiabetes; it requires the use of a 2-hour oral glucose tolerance testfor its detection. However, it has been shown that impaired glucosetolerance is by itself entirely asymptomatic and unassociated with anyfunctional disability. Indeed, insulin secretion is typically greater inresponse to a mixed meal than in response to a pure glucose load; as aresult, most persons with impaired glucose tolerance are rarely, ifever, hyperglycemic in their daily lives, except when they undergodiagnostic glucose tolerance tests. Thus, the importance of impairedglucose tolerance resides exclusively in its ability to identify personsat increased risk of future disease (Stern et al., 2002).

Diabetes is generally diagnosed by determining blood glucose levelsafter fasting overnight (fasting plasma glucose level) or by determiningblood glucose levels after fasting, followed by ingestion of glucose anda blood glucose measurement two hours after glucose administration (aglucose tolerance test). In studies conducted by Stern and colleagues(Stern et al., Diabetes Care 25:1851-1856 (2002)), the sensitivity andfalse-positive rates of impaired glucose tolerance as a predictor offuture conversion to Type 2 Diabetes was 50.9% and 10.2%, respectively,representing an area under the Receiver-Operating Characteristic Curveof 77.5% (with a 95% confidence interval of 74.3-80.7%) and a P-value(calculated using Hosmer-Lemeshow goodness-of-fit) of 0.20. Because ofthe inconvenience associated with the two-hour glucose tolerance test,as well as the cost of the test, the test is seldom used in routineclinical practice. Moreover, patients whose Diabetes is diagnosed solelyon the basis of an oral glucose tolerance test have a high rate ofreversion to normal on follow-up and may in fact representfalse-positive diagnoses (Burke et al., Diabetes Care 21:1266-1270(1998)). Stern and others reported that such cases were almost 5 timesmore likely to revert to non-diabetic status after 7 to 8 years offollow-up compared with persons meeting conventional fasting or clinicaldiagnostic criteria.

Beyond glucose and HBA1c, several single time point biomarkermeasurements have been attempted for the use of risk assessment forfuture Diabetes. U.S. Patent Application No. 2003/0100486 proposesC-Reactive Protein (CRP) and Interleukin-6 (IL-6), both markers ofsystemic inflammation, used alone and as an adjunct to the measurementof HBA1c. However, for practical reasons relating to clinicalperformance, specifically poor specificity and high false positiverates, these tests have not been adopted.

Often a person with impaired glucose tolerance will be found to have atleast one or more of the common arteriovascular disease risk factors(e.g., dyslipidemia and hypertension). This clustering has been termed“Syndrome X” or “Metabolic Syndrome” by some researchers, and can beindicative of a diabetic or pre-diabetic condition. Alone, eachcomponent of the cluster conveys increased arteriovascular and diabeticdisease risk, but together as a combination they become much moresignificant. This means that the management of persons withhyperglycemia and other features of Metabolic Syndrome should focus notonly on blood glucose control, but also include strategies for reductionof other arteriovascular disease risk factors. Furthermore, such riskfactors are non-specific for Diabetes or pre-Diabetes and are not inthemselves a basis for a diagnosis of Diabetes, or of diabetic status.

Risk prediction for Diabetes, pre-Diabetes, or a pre-diabetic conditioncan also encompass multi-variate risk prediction algorithms and computedindices that assess and estimate a subject's absolute risk fordeveloping Diabetes, pre-Diabetes, or a pre-diabetic condition withreference to a historical cohort. Risk assessment using such predictivemathematical algorithms and computed indices has increasingly beenincorporated into guidelines for diagnostic testing and treatment, andencompass indices obtained from and validated with, inter alia,multi-stage, stratified samples from a representative population. Aplurality of conventional Diabetes risk factors is incorporated intopredictive models. A notable example of such algorithms include theFramingham Study (Kannel, W. B. et al. (1976) Am. J. Cardiol. 38: 46-51)and modifications of the Framingham Study, such as the NationalCholesterol Education Program Expert Panel on Detection, Evaluation, andTreatment of High Blood Cholesterol in Adults (Adult Treatment PanelIII).

Other Diabetes risk prediction algorithms include, without limitation,the San Antonio Heart Study (Stern, M. P. et al. (1984) Am. J.Epidemiol. 120: 834-851; Stern, M. P. et al. (1993) Diabetes 42:706-714; Burke, J. P. et al. (1999) Arch. Intern. Med. 159: 1450-1456),Archimedes (Eddy, D. M. and Schlessinger, L. (2003) Diabetes Care26(11):3093-3101; Eddy, D. M. and Schlessinger, L. (2003) Diabetes Care26(11):3102-3110), the Finnish-based Diabetes Risk Score (Lindström, J.and Tuomilehto, J. (2003) Diabetes Care 26(3): 725-731), and the ElyStudy (Griffin, S. J. et al. (2000) Diabetes Metab. Res. Rev.16:164-171), the contents of which are expressly incorporated herein byreference.

Despite the numerous studies and algorithms that have been used toassess the risk of Diabetes, pre-Diabetes, or a pre-diabetic condition,a need exists for accurate methods of assessing such risks orconditions. Furthermore, due to issues of practicality and thedifficulty of the risk computations involved, there has been littleadoption of such an approach by the primary care physician that is mostlikely to initially encounter the pre-diabetic or undiagnosed earlydiabetic. Clearly, there remains a need for more practical methods ofassessing the risk of future Diabetes.

It is well documented that pre-Diabetes can be present for ten or moreyears before the detection of glycemic disorders like Diabetes.Treatment of pre-diabetics with drugs such as acarbose, metformin,troglitazone and rosiglitazone can postpone or prevent Diabetes; yet fewpre-diabetics are treated. A major reason, as indicated above, is thatno simple and unambiguous laboratory test exists to determine the actualrisk of an individual to develop Diabetes. Furthermore, even inindividuals known to be at risk of Diabetes, glycemic control remainsthe primary therapeutic monitoring endpoint, and is subject to the samelimitations as its use in the prediction and diagnosis of frankDiabetes. Thus, there remains a need in the art for methods ofidentifying, diagnosing, and treating these individuals who are not yetdiabetics, but who are at significant risk of developing Diabetes.

Tethys Bioscience continues to develop predicted test for Diabetes basedon protein biomarkers, e.g. see WO 2007/044860.

Type 2 Diabetes and Lipid Metabolism

More than one mechanism for the development of Type 2 diabetes exists.While all of the genetic causes and environmental factors involved indevelopment of insulin resistance are unknown, impaired lipid metabolismhas been shown to play an important role in the development of Type 2diabetes. Increased fasting plasma fatty acids are correlated with thedevelopment of obesity and insulin resistance in many populations andare an independent predictor of the development of Type 2 diabetes.

One hypothesis for the development of increased plasma fatty acids andinsulin resistance starts with the adipose tissue. Enlarged adipocytesrelease inflammatory cytokines into the plasma which feed back to alterthe adipose and other tissues' response to insulin. As the adipocytesbecome insulin resistant, they are unable to suppress lipolysis inresponse to insulin. These adipocytes are also unable to storeadditional fat, consequently reducing the uptake of fatty acids after ameal, resulting in excess fatty acids in the plasma. The overwhelmingamount of fatty acids released by adipose tissue chronically increasesplasma levels and diverts lipid into other tissues including liver,muscle, and pancreas.

In the liver, the increased fatty acids stimulate gluconeogenesis andglucose output from the liver. Chronic hyperinsulinemia and high plasmaglucose concentrations stimulate liver de novo production of fattyacids. While the actual amount of fatty acids produced de novo is small,the conditions that increase fatty acid production also decrease liverfatty acid oxidation. This results in higher triglyceride esterificationrates and increased availability of triglyceride for very low densitylipoprotein synthesis and secretion. Along with the additional availablesubstrate, decreased hepatocyte responsiveness to insulin may alsoincrease release of very low density lipoprotein. The additionallipoprotein lipid released from the liver becomes substrate for lipaseactivity and release of free fatty acids into the plasma creating apositive feedback loop.

In the muscle, increased free fatty acids and intramuscular lipid isstrongly correlated with impaired glucose metabolism. The muscleresponds to chronically increased plasma fatty acids by decreasingglucose uptake, thus increasing fasting and postprandial plasma glucoseconcentrations. Muscle tissue may also increase uptake and decreaseoxidation of the fatty acids, resulting in increased intramuscularlipid. The decreased oxidative capacity of the muscle is due todysfunctional mitochondria, although whether this is caused by theinsulin resistant state, or a cause of it, is unknown.

Peripheral insulin resistance can exist without the development of overtdiabetes. Development of Type 2 diabetes occurs when the pancreaticβ-cells fail to compensate for insulin resistance by increasing insulinoutput. The progression to diabetes is accompanied by loss of pancreaticβ-cells as well as an increase in the basal rate of insulin secretion bythe remaining cells, and the inability of these cells to respond toglucose. The loss of function and cell death is due to chronic exposureof β-cells to high levels of both fatty acids and glucose. Similar tothe muscle, β-cells exposed to high concentrations of fatty acids havedecreased lipid oxidation and increased intracellular triglycerides.

Type 2 diabetes is a disease of lipid metabolism as well as glucosemetabolism. While there are multiple mechanisms for the development ofinsulin resistance and Type 2 diabetes, alterations in lipid metabolismis a common theme. Even though there are differences between individualsand groups of individuals in exactly how lipid metabolism is altered,disordered lipid storage and metabolism occurs at very early stages ofinsulin resistance in all individuals with insulin resistance and couldbe considered a marker of the disease. By monitoring lipid metabolitesand whole-body lipid metabolism, it may be possible to define thealterations in lipids that occur with insulin resistance and Type 2diabetes, segregate groups of patients by their changed lipidmetabolism, and predict who would respond to therapy. Some lipids havebeen identified which predict the development of insulin resistance ordiagnosis of insulin sensitivity. However, the combination of specificlipids that improves the prediction of insulin resistance or diagnosisof a diabetic condition has not been previously shown.

What is needed are better testing methods that can be used to classify,diagnose, and monitor patients at risk of developing diabetes.

SUMMARY OF INVENTION

The instant invention relates to use of biomarkers, including proteinbiomarker and lipid metabolite biomarkers, for evaluating the risk thatan individual will become diabetic, or for identifying members of apopulation at risk of developing Diabetes, and methods of calculatingsuch risks, advising individuals of such risks, providing diagnostictest systems for calculating such risks, and various other embodimentsas described herein.

The invention provides for a method of evaluating risk for developing adiabetic condition, the method comprising: (a) obtaining biomarkermeasurement data for an individual, wherein the biomarker measurementdata is representative of measurements of biomarkers in at least onebiological sample from the individual; wherein said biomarkers comprise:(i) glucose, (ii) at least three protein biomarkers selected from theprotein biomarkers in Table 1 and (iii) at least one lipid metaboliteselected from the lipid metabolites in Table 2; and (b) evaluating riskfor the individual developing a diabetic condition based on an outputfrom a model, wherein the model is executed based on an input of thebiomarker measurement data.

In one embodiment, the obtaining step of this method comprises measuringthe biomarkers in the at least one biological sample. Furthermore, thesemethods may further comprise a step, prior to the measuring thebiomarkers, of obtaining at least one biological sample from theindividual. In another embodiment, the step of obtaining biomarkermeasurement data comprises obtaining data representative of ameasurement of the level of at least one biomarker from a preexistingrecord.

In addition, the invention provides for any of the preceding methodswherein the evaluating step includes comparing the biomarker measurementdata from the individual with biomarker measurement data of the samebiomarkers from a population, and evaluating risk for the individualdeveloping a diabetic condition from the comparison.

In a related embodiment, any of the preceding methods further comprisethe step of displaying the risk evaluation from step (b) on a visualdisplay. In addition, any of the preceding methods further compriseprinting or storing the risk evaluation on paper or an electronicstorage medium. In a further embodiment, any of the preceding methodsfurther comprise advising said individual or a health care practitionerof said risk evaluation.

The invention also provides for any of the preceding methods thatfurther comprise the step of obtaining clinical measurement data for theindividual for at least one clinical parameter selected from the groupconsisting of age, body mass index (BMI), diastolic blood pressure(DBP), family history (FHX), past gestational diabetes mellitus (GDM),height (HT), hip circumference (Hip), race, sex, systolic blood pressure(SBP), waist circumference (Waist), and weight (WT), wherein the modelis executed based on an input of the biomarker measurement data and theclinical measurement data.

The invention also provides for a method of evaluating risk fordeveloping a diabetic condition, the method comprising: (a) obtainingmeasurements of biomarkers from at least one biological sample isolatedfrom an individual, wherein said biomarkers comprise: (i) glucose, (ii)at least three protein biomarkers selected from the protein biomarkersin Table 1 and (iii) at least one lipid metabolite selected from thelipid metabolites in Table 2; and (b) calculating a risk for developinga diabetic condition from the output of a model, wherein the inputs tosaid model comprise said measurements of biomarkers, and wherein saidmodel was developed by fitting data from a longitudinal study of apopulation of individuals and said fitted data comprises levels of saidbiomarkers and conversion to Diabetes in said selected population ofindividuals. In one embodiment, the obtaining step comprises measuringthe biomarkers in the at least one biological sample.

In another embodiment, the preceding methods further comprise displayingthe calculated risk from step (b) on a visual display. In a furtherembodiment, the preceding methods further comprise printing or storingthe calculated risk on paper or an electronic storage medium. Inaddition, the preceding methods may further comprise advising saidindividual or a health care practitioner of said risk evaluation.

In a related embodiment, any of the preceding methods further comprise astep of obtaining at least one clinical measurement for the individualfor at least one clinical parameter selected from the group consistingof age, body mass index (BMI), diastolic blood pressure (DBP), familyhistory (FHX), past gestational diabetes mellitus (GDM), height (HT),hip circumference (Hip), race, sex, systolic blood pressure (SBP), waistcircumference (Waist), and weight (WT), wherein the inputs to the modelfurther comprise said at least one clinical measurement.

The biomarker measurement data or biomarker measurements of any of themethods of the invention may be obtained from an individual that has notbeen previously diagnosed as having Diabetes, pre-Diabetes, or apre-diabetic condition. Alternatively, the biomarker measurement data orbiomarker measurements of any of the methods of the invention may beobtained from an individual that has a pre-diabetic condition, and themethod evaluates or calculates risk for the individual developingDiabetes. The individual from which the biomarker measurement data orbiomarker measurements are obtained may be pregnant.

The invention provides for any of the methods of the invention, thediabetic condition is selected from the group consisting of Type 2Diabetes, pre-Diabetes, Metabolic Syndrome, Impaired Glucose Tolerance,and Impaired Fasting Glycemia.

The invention also provides for any of the methods of the invention,wherein at least one biological sample comprises whole blood, serum, orplasma. In addition, the invention provides for any of the methods ofthe invention wherein at least one of said biomarker measurements isobtained by a method selected from the group consisting of immunoassay'sand enzymatic activity assay's.

In another embodiment, the invention provides for any method of theinvention, wherein the method using said biomarkers has an area underthe ROC curve, reflecting the degree of diagnostic accuracy forpredicting development of the diabetic condition, of at least 0.75,0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, or 0.85. Inaddition, the invention provides for any method of the invention whereinthe method using said biomarkers has an area under the ROC curve,reflecting the degree of diagnostic accuracy for predicting developmentof the diabetic condition, of at least 0.02, 0.03, 0.04, 0.05, 0.06,0.07, 0.08, 0.09, 0.10, 0.11, 0.12, 0.13, 0.14, or 0.15 greater than acorresponding method wherein the biomarkers consist of the glucose andthe protein biomarkers but not the lipid metabolites.

In a further embodiment, the invention provides for a kit comprisingreagents for measuring a group of biomarkers, wherein the biomarkersare: (i) glucose, (ii) at least three protein biomarkers selected fromthe protein biomarkers in Table 1 and (iii) at least one lipidmetabolite selected from the lipid metabolites in Table 2. In addition,the invention provides for kits wherein one of the reagents comprises adetectable label. Furthermore, the invention provides for kits whereinthe reagents for the protein biomarkers and lipid metabolites areattached to a solid support.

The invention also provides for a computer-readable medium havingcomputer executable instructions for evaluating risk for developing adiabetic condition, the computer-readable medium comprising: a routine,stored on the computer-readable medium and adapted to be executed by aprocessor, to store biomarker measurement data representing measurementsof at least the following: (i) glucose, (ii) at least three proteinbiomarkers selected from the protein biomarkers in Table 1 and (iii) atleast one lipid metabolite selected from the lipid metabolites in Table2; and a routine stored on the computer-readable medium and adapted tobe executed by a processor to analyze the biomarker measurement data toevaluate a risk for developing a diabetic condition.

In another embodiment, the invention provides for a medical diagnostictest system for evaluating risk for developing a diabetic condition, thesystem comprising: a data collection tool adapted to collect biomarkermeasurement data representative of measurements of biomarkers in atleast one biological sample from an individual, wherein said biomarkerscomprise: (i) glucose, (ii) at least three protein biomarkers selectedfrom the protein biomarkers in Table 1 and (iii) at least one lipidmetabolite selected from the lipid metabolites in Table 2; and ananalysis tool comprising a statistical analysis engine adapted togenerate a representation of a correlation between a risk for developinga diabetic condition and measurements of the biomarkers, wherein therepresentation of the correlation is adapted to be executed to generatea result; and an index computation tool adapted to analyze the result todetermine the individual's risk for developing a diabetic condition andrepresent the result as an index value.

The invention also provides for the medical diagnostic test system,wherein the analysis tool comprises a first analysis tool comprising afirst statistical analysis engine, the system further comprising asecond analysis tool comprising a second statistical analysis engineadapted to select the representation of the correlation between the riskfor developing a diabetic condition and measurements of the biomarkersfrom among a plurality of representations capable of representing thecorrelation. In addition, the systems of the invention may furthercomprise a reporting tool adapted to generate a report comprising theindex value.

In another embodiment, the invention provides for a method of developinga model for evaluation of risk for developing a diabetic condition, themethod comprising: obtaining biomarker measurement data, wherein thebiomarker measurement data is representative of measurements ofbiomarkers from a population and includes endpoints of the population;wherein said biomarkers for which measurement data is obtained comprise:(i) glucose, (ii) at least three protein biomarkers selected from theprotein biomarkers in Table 1 and (iii) at least one lipid metaboliteselected from the lipid metabolites in Table 2; inputting the biomarkermeasurement data of at least a subset of the population into a model;and training the model for endpoints using the inputted biomarkermeasurement data to derive a representation of a correlation between arisk of developing a diabetic condition and measurements of biomarkersin at least one biological sample from an individual.

The invention also provides for a method of evaluating the currentstatus of a diabetic condition in an individual, the method comprising:obtaining biomarker measurement data, wherein the biomarker measurementdata is representative of measurements of biomarkers in at least onebiological sample from the individual, wherein said biomarkers comprise:(i) glucose, (ii) at least three protein biomarkers selected from theprotein biomarkers in Table 1 and (iii) at least one lipid metaboliteselected from the lipid metabolites in Table 2; and evaluating thecurrent status of a diabetic condition in the individual based on anoutput from a model, wherein the model is executed based on an input ofthe biomarker measurement data.

In another embodiment, the invention provides for a method of evaluatinga diabetic disease surrogate endpoint an individual, the methodcomprising: obtaining biomarker measurement data, wherein the biomarkermeasurement data is representative of measurements of biomarkers in atleast one biological sample from the individual; wherein said biomarkerscomprise: (i) glucose, (ii) at least three protein biomarkers selectedfrom the protein biomarkers in Table 1 and (iii) at least one lipidmetabolite selected from the lipid metabolites in Table 2; andevaluating a diabetic disease surrogate endpoint in the individual basedon an output from a model, wherein the model is executed based on aninput of the biomarker measurement data.

In one embodiment, the methods, kits, computer-readable medium, orsystems of the invention include those wherein said biomarkers compriseat least four, at least five, at least six, at least seven, at leasteight, at least nine, or at least ten protein biomarkers from Table 1.

In other embodiments, the method, kit, computer-readable medium, orsystems of the invention include those wherein said at least threeprotein biomarkers are selected from the group consisting ofadiponectin, C-reactive protein (CRP), HbA1c, IGFBP1, IGFBP2, Insulin,IL2RA, ferritin, and LEP.

In addition, the methods, kits, computer-readable medium, or systems ofthe invention include those wherein said at least three proteinbiomarkers are selected from the group consisting of: adiponectin,C-reactive protein (CRP), IL2RA, ferritin, insulin, and HbA1c.

In another embodiment, the methods, kits, computer-readable medium, orsystems of the invention include those wherein said at least threeprotein biomarkers include at least one glycemic index marker selectedfrom insulin and HbA1c.

The invention also provides for methods, kits, computer-readable medium,or systems, wherein said at least three protein biomarkers compriseadiponectin, insulin, and C-reactive protein.

The invention also provides for methods, kits, computer-readable medium,or systems, wherein said at least three protein biomarkers compriseadiponectin, CRP and HbA1c.

In another embodiment, the invention provides for methods, kits,computer-readable medium, or systems, wherein said at least threeprotein biomarkers are selected from the combinations of any one ofFIGS. 8-26.

The invention also provides for methods, kits, computer-readable medium,or systems, wherein said at least three protein markers and at least onelipid metabolite are selected from the combinations of any one of FIGS.27-35.

In one embodiment of the invention, the methods, kits, computer-readablemedium or systems are those wherein said biomarkers comprise at leasttwo, at least three, at least four, at least five, at least six, atleast seven, at least eight, at least nine, or at least ten lipidmetabolites from Table 2.

The invention also provides for methods, kits, computer-readable medium,or systems wherein said at least one lipid metabolite comprises at leastone cholesterol ester.

In another embodiment, the invention provides for methods, kits,computer-readable medium, or systems, wherein said at least one lipidmetabolite comprises at least one lipid metabolite selected from thegroup consisting of AC6:0, AC8:0, AC10:0, CE16:0, CE16:1n7, CE18:0,CE18:3n6, CE18:1n9, CE 18:2n6, CE20:3n6, CE20:4n3, TGTL, DG16:0, DG18:0,DG18:19, DG18:2n6, DG18:3n3, DGTL, FA16:0, FA16:17, FA18:1n9, FA18:2n6,FA24:0, LY16:1n7, LY18:1n7, LY18:1n9, LY18:2n6, PC16:1n7, PC18:2n6,PC18:3n6, PC18:1n7, PC20:3n9, PC22:4n6, PC22:5n3, PCdm18:0, PCdm18:1n9,PCdm16:0, PC20:3n6, PC20:4n3, PEdm18:1n9, PE16:1n7, PE18:2n6, PE20:2n6,PE22:0, PE24:1n9 PEdml8:0, TG16:0, TG16:1n7, TG18:0, TG18:1n7, TG18:1n9,TG18:2n6 and TG18:3n3.

In one embodiment, the invention provides for methods, kits,computer-readable medium, or systems, wherein said at least one lipidmetabolite selected from the group consisting of CE16:1n7, CE20:3n6,CE18:2n6, CE16:0, CE18:1n9, LY18:2n6, LY18:1n7 and LY18:1n9.

In another embodiment, the invention provides for methods, kits,computer-readable medium, or systems, wherein said at least one lipidmetabolite comprises CE 16:1n7.

The invention also provides for methods, kits, computer-readable medium,or systems, wherein said at least one lipid metabolite comprises CE20:3n6.

The invention also provides for methods, kits, computer-readable medium,or systems, wherein said at least one lipid metabolite comprisesCE18:2n6.

The invention also provides for methods, kits, computer-readable medium,or systems, wherein said at least one lipid metabolite comprises CE16:0.

The invention also provides for methods, kits, computer-readable medium,or systems, wherein said at least one lipid metabolite comprisesCE18:1n9.

The invention also provides for methods, kits, computer-readable medium,or systems, wherein said at least one lipid metabolite comprisesLY18:2n6.

The invention also provides for methods, kits, computer-readable medium,or systems, wherein said at least one lipid metabolite comprisesLY18:1n7 or LY18:1n9.

In one embodiment, the invention provides for a method of prophylaxisfor Diabetes comprising: obtaining risk score data representing aDiabetes risk score for an individual, wherein the Diabetes risk scoreis computed according to a method of the invention for calculating arisk of developing a diabetic condition; and generating prescriptiontreatment data representing a prescription for a treatment regimen todelay or prevent the onset of Diabetes to an individual identified bythe Diabetes risk score as being at elevated risk for Diabetes.

In a related embodiment, the invention provides for a method ofprophylaxis for Diabetes comprising: evaluating or calculating risk, forat least one subject, of developing a diabetic condition according toany method of the invention; and treating a subject identified as beingat elevated risk for a diabetic condition with a treatment regimen todelay or prevent the onset of Diabetes.

In the preceding methods of prophylaxis for Diabetes, the treatmentregimen comprises at least one therapeutic selected from the groupconsisting of: INS, INS analogs, hypoglycemic agents, anti-inflammatoryagents, lipid-reducing agents, calcium channel blockers, beta-adrenergicreceptor blocking agents, cyclooxygenase-2 (COX-2) inhibitors, prodrugsof COX-2 inhibitors, angiotensin II antagonists, angiotensin-convertingenzyme (ACE) inhibitors, renin inhibitors, lipase inhibitors, amylinanalogs, sodium-glucose cotransporter 2 inhibitors, dual adiposetriglyceride lipase and PI3 kinase activators, antagonists ofneuropeptide Y receptors, human hormone analogs, cannabinoid receptorantagonists, triple monoamine oxidase reuptake inhibitors, inhibitors ofnorepinephrine and dopamine reuptake, inhibitors of 11Beta-hydroxysteroid dehydrogenase type 1 (11b-HSD1), inhibitors ofcortisol synthesis, inhibitors of gluconeogenesis, glucokinaseactivators, antisense inhibitors of protein tyrosine phosphatase-1B,islet neogenesis therapy, and betahistine. In addition, in the method ofphophylaxis for Diabetes the treatment region comprises at least onetherapeutic selected from the group consisting of acarbose, metformin,troglitazone, and rosiglitazone.

In another embodiment, the invention provides a method of ranking orgrouping a population of individuals, comprising: calculating fordeveloping a diabetic condition according to the any method of theinvention for individuals comprised within the population; and rankingindividuals within the population relative to the remaining individualsin the population or dividing the population into at least two groups,based on factors comprising said risk for developing a diabeticcondition.

In a further embodiment, the methods of ranking or grouping populationsof individuals further comprises using ranking data representing theranking or grouping of the population of individuals for one or more ofthe following purposes: to determine an individual's eligibility forhealth insurance; to determine an individual's premium for healthinsurance; to determine an individual's premium for membership in ahealth care plan, health maintenance organization, or preferred providerorganization; and to assign health care practitioners to an individualin a health care plan, health maintenance organization, or preferredprovider organization.

The invention also provides for methods of ranking or groupingindividuals, further comprising using ranking data representing theranking or grouping of the population of individuals for one or morepurposes selected from the group consisting of: to recommend therapeuticintervention or lifestyle intervention to an individual or group ofindividuals; to manage the health care of an individual or group ofindividuals; to monitor the health of an individual or group ofindividuals; and to monitor the health care treatment, therapeuticintervention, or lifestyle intervention for an individual or group ofindividuals.

In an embodiment, the invention provides for a method of evaluating thecurrent status of a diabetic condition in an individual, the methodcomprising: obtaining biomarker measurement data, wherein the biomarkermeasurement data is representative of measurements of biomarkers in atleast one biological sample from the individual; and evaluating thecurrent status of a diabetic condition in the individual based on anoutput from a model, wherein the model is executed based on an input ofthe biomarker measurement data; wherein said biomarkers comprise: (i)glucose, (ii) at least three protein biomarkers selected from theprotein biomarkers in Table 1 and (iii) at least one lipid metaboliteselected from the lipid metabolites in Table 2.

The foregoing summary is not intended to define every aspect of theinvention, and additional aspects are described in other sections, suchas the Detailed Description. The entire document is intended to berelated as a unified disclosure, and it should be understood that allcombinations of features described herein are contemplated, even if thecombination of features are not found together in the same sentence, orparagraph, or section of this document.

In addition to the foregoing, the invention includes, as an additionalaspect, all embodiments of the invention narrower in scope in any waythan the variations specifically mentioned above. With respect toaspects of the invention described as a genus, all individual speciesare individually considered separate aspects of the invention. Withrespect to aspects described as a range, all sub-ranges and individualvalues are specifically contemplated.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 provides the performance and validation of a model to assess riskof 5-year incidence of Type 2 diabetes in the Inter99 cohort. ROC curvesfor a model that uses the levels of six biomarkers—fasting serum ADIPOQ,CRP, insulin, FTH1, and IL2RA, and fasting plasma glucose—that wasdeveloped using the entire data set (all 632 converters andnon-converters, solid line), and validated using a bootstrap re-samplingapproach (dotted line).

FIG. 2 depicts the ROC analyses for 11 methods of assessing 5-year riskfor Type 2 diabetes. DRS, diabetes risk score developed in the presentstudy; HOMA-IR, homeostasis model assessment insulin resistance (fastingserum insulin×fasting plasma glucose)/22.5); noninvasive clinical model(a non-invasive clinical algorithm using age, BMI, waist circumference,and family history in a first-degree relative); OGTT, 2-hour oralglucose tolerance test. Significance codes:0<***<0.001<**<0.01<*<0.05<<1.

FIG. 3 depicts the performance of the Diabetes Risk Score and fastingplasma glucose in the at-risk Inter99 subpopulation defined by BMI≧25kg/m² and age ≧39 years. The white, light gray and dark gray regionscorrespond to the low-, medium- and high-risk strata, respectively. Theresults from the study were adjusted using Bayes' rule to reflect theobserved five-year incidence of 5.7% among the 3032 at-risk individualsin Inter99 (FIG. 3A). On the left axis, absolute risk is indicated,while relative risk is shown on the right axis. The solid black linerepresents the relationship between risk and DRS prediction. The dashedcurves indicate mean upper and lower 95% confidence intervals on therisk, as estimated from the standard error of the individual riskpredictions in the study. The triangles represent deciles of theadjusted study population; the mean observed fraction that converted isplotted vs. mean DRS. Details of the development of this risk curve arepresented in Online Appendix C. Stratification of the at-risk Inter99subpopulation by fasting plasma glucose status (FIG. 3B), and by DRSrisk stratum (FIG. 3C). NFG, normal fasting glucose (≦100 mg/dL); IFG,impaired fasting glucose (>100 mg/dL).

FIG. 4 depicts the AUC of DRS, HbA1c, BMI, Gender-adjusted Waist,Fasting Insulin and Fasting Glucose, HOMA IR and a Non-invasive ClinicalModel

FIG. 5 displays the pathways associated with progression to diabetes.

FIG. 6 illustrates an example of a suitable computing system environment100 on which a system for the steps of the claimed method and apparatusmay be implemented.

FIG. 7 is a flow diagram of an example method for developing a modelwhich may be used to evaluate a risk of a person, or group of people,for developing a diabetic condition.

FIG. 8 is a flow diagram of an example method for using a model toevaluate a risk of a subject (e.g., a person, or group of people)developing a diabetic condition.

FIG. 9 depicts particularly useful 3-panel combinations from anevaluation of the 75 parameters.

FIG. 10 (A-I) contains tables summarizing enumeration of fitted logisticregression models for various three-panel through eleven-panel ALLDBRISKcombinations possible from a starting set of the 11 selected ALLDBRISK(Tier 1-2), as measured and calculated from the base population ofExample 2.

FIG. 10A depicts 7 particularly useful combinations of panels of threebiomarkers; each panel can be used alone, or with additional biomarkersin combination to the three markers listed.

FIG. 10B depicts 25 particularly useful combinations of panels of fourbiomarkers; each panel can be used alone, or with additional biomarkersin combination to the four markers listed.

FIG. 10C depicts 65 particularly useful combinations of panels of fivebiomarkers; each panel can be used alone, or with additional biomarkersin combination to the five markers listed.

FIG. 10D depicts 134 particularly useful combinations of panels of sixbiomarkers; each panel can be used alone, or with additional biomarkersin combination to the six markers listed.

FIG. 10E depicts 147 particularly useful combinations of panels of sevenbiomarkers; each panel can be used alone, or with additional biomarkersin combination to the seven markers listed.

FIG. 10F depicts 100 particularly useful combinations of panels of eightbiomarkers; each panel can be used alone, or with additional biomarkersin combination to the eight markers listed.

FIG. 10G depicts 44 particularly useful combinations of panels of ninebiomarkers; each panel can be used alone, or with additional biomarkersin combination to the nine markers listed.

FIG. 10H depicts 11 particularly useful combinations of panels of tenbiomarkers; each panel can be used alone, or with additional biomarkersin combination to the ten markers listed.

FIG. 10I depicts a particularly useful combination of a panel of elevenbiomarkers; the panel can be used alone, or with additional biomarkersin combination to the eleven markers listed.

FIG. 11A depicts is a table summarizing the complete enumeration offitted logistic regression models for all three-panel ALLDBRISKcombinations possible from a starting set of 26 selected ALLDBRISK (Tier1-3). FIG. 11B depicts is a table summarizing the complete enumerationof fitted logistic regression models for all four-panel ALLDBRISKcombinations possible from a starting set of 26 selected ALLDBRISK (Tier1-3). FIG. 11C depicts is a table summarizing the complete enumerationof fitted logistic regression models for all five-panel ALLDBRISKcombinations possible from a starting set of 26 selected ALLDBRISK (Tier1-3). FIG. 11D depicts is a table summarizing the complete enumerationof fitted logistic regression models for all six-panel ALLDBRISKcombinations possible from a starting set of 26 selected ALLDBRISK (Tier1-3). FIG. 11E depicts is a table summarizing the complete enumerationof fitted logistic regression models for all seven-panel ALLDBRISKcombinations possible from a starting set of 26 selected ALLDBRISK (Tier1-3).

FIG. 12 depicts selected particularly useful combinations of panels ofthree biomarkers; each panel can be used alone, or with additionalbiomarkers in combination to the three markers listed. These panelsrepresent enumeration of fitted logistic regression models from astarting set of 65 selected ALLDBRISK, as measured and calculated from alarger base population and meet a predetermined cutoff level (0.75 AUCor better).

FIG. 13 depicts selected particularly useful combinations of panels offour biomarkers; each panel can be used alone, or with additionalbiomarkers in combination to the four markers listed. These panelsrepresent enumeration of fitted logistic regression models from astarting set of 26 selected ALLDBRISK, as measured and calculated from alarger base population and meet a predetermined cutoff level (0.75 AUCor better).

FIG. 14 depicts selected particularly useful combinations of panels offive biomarkers; each panel can be used alone, or with additionalbiomarkers in combination to the five markers listed. These panelsrepresent enumeration of fitted logistic regression models from astarting set of 26 selected ALLDBRISK, as measured and calculated from alarger base population and meet a predetermined cutoff level (0.75 AUCor better).

FIG. 15 depicts selected particularly useful combinations of panels ofsix biomarkers; each panel can be used alone, or with additionalbiomarkers in combination to the six markers listed. These panelsrepresent enumeration of fitted logistic regression models from astarting set of 26 selected ALLDBRISK, as measured and calculated from alarger base population and meet a predetermined cutoff level (0.75 AUCor better).

FIG. 16 depicts selected particularly useful combinations of panels ofseven biomarkers; each panel can be used alone, or with additionalbiomarkers in combination to the seven markers listed. These panelsrepresent enumeration of fitted logistic regression models from astarting set of 26 selected ALLDBRISK, as measured and calculated from alarger base population and meet a predetermined cutoff level (0.75 AUCor better).

FIG. 17 depicts selected particularly useful combinations of panels ofeight biomarkers; each panel can be used alone, or with additionalbiomarkers in combination to the eight markers listed. These panelsrepresent enumeration of fitted logistic regression models from astarting set of 18 selected ALLDBRISK, as measured and calculated from alarger base population and meet a predetermined cutoff level (0.75 AUCor better).

FIG. 18 depicts selected particularly useful combinations of panels ofnine biomarkers; each panel can be used alone, or with additionalbiomarkers in combination to the nine markers listed. These panelsrepresent enumeration of fitted logistic regression models from astarting set of 18 selected ALLDBRISK, as measured and calculated from alarger base population and meet a predetermined cutoff level (0.75 AUCor better).

FIG. 19 depicts selected particularly useful combinations of panels often biomarkers; each panel can be used alone, or with additionalbiomarkers in combination to the ten markers listed. These panelsrepresent enumeration of fitted logistic regression models from astarting set of 185 selected ALLDBRISK, as measured and calculated froma larger base population and meet a predetermined cutoff level (0.75 AUCor better).

FIG. 20 depicts selected particularly useful combinations of panels ofeleven biomarkers; each panel can be used alone, or with additionalbiomarkers in combination to the eleven markers listed. These panelsrepresent enumeration of fitted logistic regression models from astarting set of 18 selected ALLDBRISK, as measured and calculated from alarger base population and meet a predetermined cutoff level (0.75 AUCor better).

FIG. 21 depicts selected particularly useful combinations of panels oftwelve biomarkers; each panel can be used alone, or with additionalbiomarkers in combination to the twelve markers listed. These panelsrepresent enumeration of fitted logistic regression models from astarting set of 18 selected ALLDBRISK, as measured and calculated from alarger base population and meet a predetermined cutoff level (0.75 AUCor better).

FIG. 22 depicts selected particularly useful combinations of panels ofthirteen biomarkers; each panel can be used alone, or with additionalbiomarkers in combination to the thirteen markers listed. These panelsrepresent enumeration of fitted logistic regression models from astarting set of 18 selected ALLDBRISK, as measured and calculated from alarger base population and meet a predetermined cutoff level (0.75 AUCor better).

FIG. 23 depicts selected particularly useful combinations of panels offourteen biomarkers; each panel can be used alone, or with additionalbiomarkers in combination to the fourteen markers listed. These panelsrepresent enumeration of fitted logistic regression models from astarting set of 18 selected ALLDBRISK, as measured and calculated from alarger base population and meet a predetermined cutoff level (0.75 AUCor better).

FIG. 24 depicts selected particularly useful combinations of panels offifteen biomarkers; each panel can be used alone, or with additionalbiomarkers in combination to the fifteen markers listed. These panelsrepresent enumeration of fitted logistic regression models from astarting set of 18 selected ALLDBRISK, as measured and calculated from alarger base population and meet a predetermined cutoff level (0.75 AUCor better).

FIG. 25 depicts selected particularly useful combinations of panels ofsixteen biomarkers; each panel can be used alone, or with additionalbiomarkers in combination to the sixteen markers listed. These panelsrepresent enumeration of fitted logistic regression models from astarting set of 18 selected ALLDBRISK, as measured and calculated from alarger base population and meet a predetermined cutoff level (0.75 AUCor better).

FIG. 26 depicts selected particularly useful combinations of panels ofseventeen biomarkers; each panel can be used alone, or with additionalbiomarkers in combination to the six markers listed. These panelsrepresent enumeration of fitted logistic regression models from astarting set of 18 selected ALLDBRISK, as measured and calculated from alarger base population and meet a predetermined cutoff level (0.75 AUCor better).

FIG. 27 depicts selected particularly useful combinations of panels ofglucose, at least three protein biomarkers and at least one lipidmetabolite.

FIG. 28 depicts selected particularly useful combinations of panels ofglucose, at least three protein biomarkers and at least one lipidmetabolite.

FIG. 29 depicts selected particularly useful combinations of panels ofglucose, at least three protein biomarkers and at least one lipidmetabolite.

FIG. 30 depicts selected particularly useful combinations of panels ofglucose, at least three protein biomarkers and at least one lipidmetabolite.

FIG. 31 depicts selected particularly useful combinations of panels ofglucose, at least three protein biomarkers and at least one lipidmetabolite.

FIG. 32 depicts selected particularly useful combinations of panels ofglucose, at least three protein biomarkers and at least one lipidmetabolite.

FIG. 33 depicts selected particularly useful combinations of panels ofglucose, at least three protein biomarkers and at least one lipidmetabolite.

FIG. 34 depicts selected particularly useful combinations of panels ofglucose, at least three protein biomarkers and at least one lipidmetabolite.

FIG. 35 depicts selected particularly useful combinations of panels ofglucose, at least three protein biomarkers and at least one lipidmetabolite.

DETAILED DESCRIPTION

This invention includes a set of biological markers, including proteinbiomarkers and lipid metabolites, and an associated multivariatealgorithm that provides significant discriminatory improvement whencompared to current standards of care in assessing risk for Diabetes.This set of markers incorporates clinical measures, protein biomarkers,and lipid metabolites.

A key methodological component in this invention is the extraction of arelatively few number of the most informative markers out of a large setof potential marker candidates using a stepwise procedure of univariateand multivariate performance assessment and considerations of analyticalstability/development. One noteworthy feature of this invention is thecombination of proteins and lipid metabolites (and optionally otherfactors including blood glucose) in a single multivariate algorithm thatprovides better discriminatory performance over OGTT and any singleclass of markers alone.

The present invention relates to the identification of biomarkersassociated with subjects having Diabetes, pre-Diabetes, or apre-diabetic condition, or who are pre-disposed to developing Diabetes,pre-Diabetes, or a pre-diabetic condition. Accordingly, the presentinvention features methods for identifying subjects who are at risk ofdeveloping Diabetes, pre-Diabetes, or a pre-diabetic condition,including those subjects who are asymptomatic for Diabetes,pre-Diabetes, or a pre-diabetic condition by detection of the biomarkersdisclosed herein. These biomarkers are also useful for monitoringsubjects undergoing treatments and therapies for Diabetes, pre-Diabetes,or pre-diabetic conditions, and for selecting or modifying therapies andtreatments that would be efficacious in subjects having Diabetes,pre-Diabetes, or a pre-diabetic condition, wherein selection and use ofsuch treatments and therapies slow the progression of Diabetes,pre-Diabetes, or pre-diabetic conditions, or prevent their onset.

A list of 272 biomarkers (collectively referred to as ALLDBRISK) are setout in Table 7 below; these biomarkers are analyte-based or individualhistory—based biomarkers for use in the present invention. A preferredsubset of protein biomarkers for use in the present invention are setout in Table 1.

A prioritized list of markers was developed from 3 nested case controlstudies, Inter-99 (n=632), Botnia (n=387) and Joslin (n=94). Thedifferent studies had different objectives but all created modelsrelated to the development of diabetes. In Inter-99 markers wereselected based on common marker selection techniques (e.g., stepwiseselection, backward selection, univariate significance) individually orunder 100 bootstrap replicates. Markers were ranked for each criteriaand sorted by the average rank. The average ranking was categorized intofour categories: always selected, often selected (>50% of the time),sometimes selected (<50%) and not selected. This prioritization schemewas repeated for the impaired fasting glucose subset and the normalfasting glucose subset. The Botnia data set was analyzed in a mannerconsistent with Inter-99. The Joslin data set developed three modelspredicting 2-hour glucose after an oral glucose challenge (OGTT),insulin sensitivity (CISI) and insulin resistance (Delta Insulin) andmarkers were selected using common selection techniques (univariate,forward, backward, or stepwise selection) under 100 bootstrapreplicates. Significance of marker counts was judged by repeating themarker selection criteria on many permuted outcomes. Markers weredivided into 4 categories as follows: always selected were those markersselected when all markers were allowed into the model, often selectedwere those models selected after removing glucose and insulin fromconsideration, sometimes selected were those models when all glycemicindices (Glucose, insulin, HbA1c, Fructosamine) are removed, and notselected.

TABLE 1 Preferred Protein Biomarkers Official Name Entrez Gene LinkAdiponectin, C1Q and collagen domain containing ADIPOQ Advancedglycosylation end product-specific AGER receptor Angiogenin,ribonuclease, RNase A family, 5 ANG complement component 3 C3 chemokine(C-C motif) ligand 2 CCL2 cyclin-dependent kinase 5 CDK5 C-reactiveprotein, pentraxin-related CRP Fas ligand (TNF superfamily, member 6)FASLG fibrinogen alpha chain FGA FRUCTOSAMINE Ferritin FTH1glutamic-pyruvate transaminase (alanine GPT aminotransferase) HemoglobinA1c HBA1C hepatocyte growth factor (hepapoietin A; scatter HGF factor)heat shock 70 kDa protein 1B HSPA1B insulin-like growth factor bindingprotein 1 IGFBP1 insulin-like growth factor binding protein 2 IGFBP2insulin-like growth factor binding protein 3 IGFBP3 interleukin 18(interferon-gamma-inducing factor) IL18 interleukin 2 receptor, alphaIL2RA inhibin, beta A (activin A, activin AB alpha INBHA polypeptide)insulin INSULIN-M leptin (obesity homolog, mouse) LEP matrixmetallopeptidase 9 (gelatinase B, 92 kDa MMP9 gelatinase, 92 kDa type IVcollagenase) plasminogen activator, tissue PLAT serpin peptidaseinhibitor, clade E (nexin, SERPINE1 plasminogen activator inhibitor type1), member 1 tumor necrosis factor receptor superfamily, TNFRSF1A member1A vascular endothelial growth factor VEGF von Willebrand factor VWF

In one embodiment, the invention provides novel panels of biomarkerswhich can be measured and used to evaluate the risk that an individualwill develop Diabetes in the future, for example, the risk that anindividual will develop Diabetes in the next 1, 2, 2.5, 5, 7.5, or 10years. Exemplary preferred panels are shown in FIGS. 8-26. Each paneldepicted in a Figure is contemplated as an individual embodiment of theinvention, when combined with one or more lipid metabolite biomarker asdescribed herein in detail. Each panel defines a set of markers that canbe employed with one or more lipid metabolite biomarker for methods,improvements, kits, computer-readable media, systems, and other aspectsof the invention which employ such sets of markers.

In addition, FIGS. 27-31 provide exemplary preferred combinations ofglucose, at least three protein biomarkers and at least one lipidmetabolite. Each panel depicted in a Figure is contemplated as anindividual embodiment of the invention, when combined with one or morelipid metabolite biomarker as described herein in detail. Each paneldefines a set of markers that can be employed for methods, improvements,kits, computer-readable media, systems, and other aspects of theinvention which employ such sets of markers.

A list of lipid metabolite biomarkers is set out in Table 3 below. Apreferred subset of the lipid metabolite biomarkers for use in thepresent invention is set out in Table 2:

TABLE 2 Lipid Metabolites AC6:0 AC8:0 AC10:0 CE16:0 CE16:1n7 CE18:0CE18:3n6 CE18:1n9 CE 18:2n6 CE20:3n6 CE20:4n3 TGTL DG16:0 DG18:0DG18:1n9 DG18:2n6 DG19:3n3 DGTL FA16:0 FA16:1n7 FA18:1n9 FA18:2n6 FA24:0LY16:1n7 LY18:1n7 LY18:1n9 LY18:2n6 PC16:1n7 PC18:2n6 PC18:3n6 PC18:1n7PC20:3n9 PC22:4n6 PC22:5n3 PCdm18:0 PCdm18:1n9 PCdm16:0 PC20:3n6PC20:4n3 PEdm18:1n9 PE16:1n7 PE18:2n6 PE20:2n6 PE22:0 PE24:1n9 PEdm18:0TG16:0 TG16:1n7 TG18:0 TG18:1n7 TG18:1n9 1G18:2n6 tG18:3n3

The complete disclosures of International Patent Application Nos.PCT/US2008/002357 (Lipomics Technologies, Inc., published as WO2008/106054 on 4 Sep. 2008) and PCT/US2008/060830 (Tethys Bioscience,published as WO 2008/131224 on 30 Oct. 2008); and related U.S. patentapplication Ser. Nos. 12/528,065 and 12/501,385 (Tethys, Publication No.2009/0271124, 29 Oct. 2009) are incorporated herein in their entireties.

Definitions

“A,” “an,” and “the” include plural references unless the contextclearly dictates otherwise.

As used herein, “body fluid” includes, but is not limited to, fluidssuch as blood, plasma, serum, isolated lipoprotein fractions, saliva,urine, lymph, cerebrospinal fluid, and bile.

“Lipid class,” as used herein, indicates classes of lipids such as, forexample, neutral lipids, phospholipids, free fatty acids, total fattyacids, triglycerides, cholesterol esters, phosphatidylcholines,phosphatidylethanolamines, diglycerides, lysophatidylcholines, freecholesterol, monoacylglyerides, phosphatidylglycerol,phosphatidylinositol, phosphatidylserine, and sphingomyelin.

Chemical terms, unless otherwise defined, are used as known in the art.

It is understood that wherever embodiments are described herein with thelanguage “comprising,” otherwise analogous embodiments described interms of “consisting of and/or “consisting essentially of are alsoprovided.

As used herein, metabolites (or other biomarkers) that are “positivelyassociated” or “positively correlated” with a condition or disorderinclude those metabolites whose levels or concentrations generallyincrease with the disorder relative to normal control subjects or anormal control reference. Metabolites (or other biomarkers) that are“negatively associated” or “negatively correlated” with a condition ordisorder generally include those metabolites whose levels orconcentrations decrease with the disorder relative to normal controlsubjects or a normal control reference.

“Accuracy” refers to the degree of conformity of a measured orcalculated quantity (a test reported value) to its actual (or true)value. Clinical accuracy relates to the proportion of true outcomes(true positives (TP) or true negatives (TN)) versus misclassifiedoutcomes (false positives (FP) or false negatives (FN)), and may bestated as a sensitivity, specificity, positive predictive values (PPV)or negative predictive values (NPV), or as a likelihood, odds ratio,among other measures.

“Biomarker” in the context of the present invention encompasses, withoutlimitation, proteins, nucleic acids, and metabolites, together withtheir polymorphisms, mutations, variants, modifications, subunits,fragments, protein-ligand complexes, and degradation products,protein-ligand complexes, elements, related metabolites, and otheranalytes or sample-derived measures. Biomarkers can also include mutatedproteins or mutated nucleic acids. Biomarkers also encompassnon-blood-borne factors, non-analyte physiological markers of healthstatus, or other factors or markers not measured from samples (e.g.,biological samples such as bodily fluids), such as “clinical parameters”defined herein, as well as “traditional laboratory risk factors,” alsodefined herein. Biomarkers also include any calculated indices createdmathematically or combinations of any one or more of the foregoingmeasurements, including temporal trends and differences. The term“analyte” as used herein can mean any substance to be measured and canencompass electrolytes and elements, such as calcium.

“Clinical parameters” or “CPs” encompasses all non-sample or non-analytebiomarkers of subject health status or other characteristics, such as,without limitation, age (AGE), race or ethnicity (RACE), gender (SEX),diastolic blood pressure (DBP) and systolic blood pressure (SBP), familyhistory (FHX, including FHx1 for 1 parent and FHx2 for 2 parents),height (HT), weight (WT), waist (Waist) and hip (Hip) circumference,Waist-Hip ratio (WHr), body-mass index (BMI), past Gestational DiabetesMellitus (GDM), and resting heart rate.

“Consideration” encompasses anything of value, including, but notlimited to, monetary consideration, as well as non-monetaryconsideration including, but not limited to, related services orproducts, discounts on services or products, favored supplierrelationships, more rapid reimbursements, etc.

“Diabetic condition” in the context of the present invention comprisesType 1 and Type 2 Diabetes mellitus, and pre-Diabetes (defined herein).It is also known in the art that Diabetic-related conditions includeDiabetes and the pre-diabetic condition (defined herein).

“Diabetes mellitus” in the context of the present invention encompassesType 1 Diabetes, both autoimmune and idiopathic and Type 2 Diabetes(referred to herein as “Diabetes” or “T2DM”). The World HealthOrganization defines the diagnostic value of fasting plasma glucoseconcentration to 7.0 mmol/1 (126 mg/dl) and above for Diabetes mellitus(whole blood 6.1 mmol/1 or 110 mg/dl), or 2-hour glucose level greaterthan or equal to 11.1 mmol/L (greater than or equal to 200 mg/dL). Itmay also be possible to diagnose Diabetes based on an HbA1c level ofgreater than 6%, for instance, ≧6.5%. Other values suggestive of orindicating high risk for Diabetes mellitus include elevated arterialpressure greater than or equal to 140/90 mm Hg; elevated plasmatriglycerides (greater than or equal to 1.7 mmol/L; 150 mg/dL) and/orlow HDL-cholesterol (<0.9 mmol/L, 35 mg/dL for men; <1.0 mmol/L, 39mg/dL for women); central obesity (males: waist to hip ratio >0.90;females: waist to hip ratio >0.85) and/or body mass index exceeding 30kg/m2; microalbuminuria, where the urinary albumin excretion rate isgreater than or equal to 20 μg/min or albumin creatinine ratio greaterthan or equal to 30 mg/g).

“Gestational Diabetes” refers to glucose intolerance during pregnancy.This condition results in high blood sugar that starts or is firstdiagnosed during pregnancy.

“FN” is false negative, which for a disease state test means classifyinga disease subject incorrectly as non-disease or normal.

“FP” is false positive, which for a disease state test means classifyinga normal subject incorrectly as having disease.

The terms “formula,” “algorithm,” and “model” are used interchangeablyfor any mathematical equation, algorithmic, analytical or programmedprocess, or statistical technique that takes one or more continuous orcategorical inputs (herein called “parameters”) and calculates an outputvalue, sometimes referred to as an “index” or “index value.”Non-limiting examples of “formulas” include sums, ratios, and regressionoperators, such as coefficients or exponents, biomarker valuetransformations and normalizations (including, without limitation, thosenormalization schemes based on clinical parameters, such as gender, age,or ethnicity), rules and guidelines, statistical classification models,and neural networks trained on historical populations. Of particular usefor the biomarkers are linear and non-linear equations and statisticalclassification analyses to determine the relationship between levels ofbiomarkers detected in a subject sample and the subject's risk ofDiabetes. In panel and combination construction, of particular interestare structural and syntactic statistical classification algorithms, andmethods of risk index construction, utilizing pattern recognitionfeatures, including established techniques such as cross-correlation,Principal Components Analysis (PCA), factor rotation, LogisticRegression (LogReg), Linear Discriminant Analysis (LDA), EigengeneLinear Discriminant Analysis (ELD A), Support Vector Machines (SVM),Random Forest (RF), Recursive Partitioning Tree (RPART), as well asother related decision tree classification techniques, Shruken Centroids(SC), StepAIC, Kth-Nearest Neighbor, Boosting, Decision Trees, NeuralNetworks, Bayesian Networks, Support Vector Machines, and Hidden MarkovModels, Linear Regression or classification algorithms, NonlinearRegression or classification algorithms, analysis of variants (ANOVA),hierarchical analysis or clustering algorithms; hierarchical algorithmsusing decision trees; kernel-based machine algorithms such as kernelpartial least squares algorithms, kernel matching pursuit algorithms,kernel Fisher's discriminate analysis algorithms, or kernel principalcomponents analysis algorithms, among others. Many of these techniquesare useful either combined with a ALLDBRISK selection technique, such asforward selection, backward selection, or stepwise selection, completeenumeration of all potential panels of a given size, genetic algorithms,or they may themselves include biomarker selection methodologies intheir own technique. These may be coupled with information criteria,such as Akaike's Information Criterion (AIC) or Bayes InformationCriterion (BIC), in order to quantify the tradeoff between additionalbiomarkers and model improvement, and to aid in minimizing overfit. Theresulting predictive models may be validated in other studies, orcross-validated in the study they were originally trained in, using suchtechniques as Leave-One-Out (LOO) and 10-Fold cross-validation (10-FoldCV). A “DRS Formula” is a formula developed as described herein and usedto calculate a Diabetes risk score from inputs comprising the resultsfrom biomarker testing as described herein. A DRS Formula is thepreferred means for calculating a Diabetes risk score.

A “Health economic utility function” is a formula that is derived from acombination of the expected probability of a range of clinical outcomesin an idealized applicable patient population, both before and after theintroduction of a diagnostic or therapeutic intervention into thestandard of care. It encompasses estimates of the accuracy,effectiveness and performance characteristics of such intervention, anda cost and/or value measurement (a utility) associated with eachoutcome, which may be derived from actual health system costs of care(services, supplies, devices and drugs, etc.) and/or as an estimatedacceptable value per quality adjusted life year (QALY) resulting in eachoutcome. The sum, across all predicted outcomes, of the product of thepredicted population size for an outcome multiplied by the respectiveoutcome's expected utility is the total health economic utility of agiven standard of care. The difference between (i) the total healtheconomic utility calculated for the standard of care with theintervention versus (ii) the total health economic utility for thestandard of care without the intervention results in an overall measureof the health economic cost or value of the intervention. This mayitself be divided among the entire patient group being analyzed (orsolely amongst the intervention group) to arrive at a cost per unitintervention, and to guide such decisions as market positioning,pricing, and assumptions of health system acceptance. Such healtheconomic utility functions are commonly used to compare thecost-effectiveness of the intervention, but may also be transformed toestimate the acceptable value per QALY the health care system is willingto pay, or the acceptable cost-effective clinical performancecharacteristics required of a new intervention.

For diagnostic (or prognostic) interventions of the invention, as eachoutcome (which in a disease-classifying diagnostic test may be a TP, FP,TN, or FN) bears a different cost, a health economic utility functionmay preferentially favor sensitivity over specificity, or PPV over NPV,based on the clinical situation and individual outcome costs and value,and thus provides another measure of health economic performance andvalue which may be different from more direct clinical or analyticalperformance measures. These different measurements and relativetrade-offs generally will converge only in the case of a perfect test,with zero error rate (aka zero predicted subject outcomemisclassifications or FP and FN), which all performance measures willfavor over imperfection, but to differing degrees.

“Impaired glucose tolerance” (IGT) is a pre-diabetic condition definedas having a blood glucose level that is higher than normal, but not highenough to be classified as Diabetes Mellitus. A subject with IGT willhave two-hour glucose levels of 140 to 199 mg/dL (7.8 to 11.0 mmol) onthe 75-g oral glucose tolerance test. These glucose levels are abovenormal but below the level that is diagnostic for Diabetes. Subjectswith impaired glucose tolerance or impaired fasting glucose have asignificant risk of developing Diabetes and thus are an important targetgroup for primary prevention.

“Insulin resistance” refers to a diabetic or pre-diabetic condition inwhich the cells of the body become resistant to the effects of insulin,that is, the normal response to a given amount of insulin is reduced. Asa result, higher levels of insulin are needed in order for insulin toexert its effects.

The oral glucose tolerance test (OGTT) is principally used for diagnosisof Diabetes Mellitus or pre-diabetic conditions when blood glucoselevels are equivocal, during pregnancy, or in epidemiological studies(Definition, Diagnosis and Classification of Diabetes Mellitus and itsComplications, Part 1, World Health Organization, 1999). The OGTT shouldbe administered in the morning after at least 3 days of unrestricteddiet (greater than 150 g of carbohydrate daily) and usual physicalactivity. A reasonable (30-50 g) carbohydrate-containing meal should beconsumed on the evening before the test. The test should be preceded byan overnight fast of 8-14 hours, during which water may be consumed.After collection of the fasting blood sample, the subject should drink75 g of anhydrous glucose or 82.5 g of glucose monohydrate in 250-300 mLof water over the course of 5 minutes. For children, the test loadshould be 1.75 g of glucose per kg body weight, up to a total of 75 g ofglucose. Timing of the test is from the beginning of the drink. Bloodsamples must be collected 2 hours after the test load. As previouslynoted, a diagnosis of impaired glucose tolerance (IGT) has been noted asbeing only 50% sensitive, with a >10% false positive rate, for a7.5-year conversion to Diabetes when used at the WHO cutoff points. Thisis a significant problem for the clinical utility of the test, as evenrelatively high risk ethnic groups have only a 10% rate of conversion toDiabetes over such a period unless otherwise enriched by other riskfactors; in an unselected general population, the rate of conversionover such periods is typically estimated at 5-6%, or less than 1% perannum.

“Measuring” or “measurement” means assessing the presence, absence,quantity or amount (which can be an effective amount) of either a givensubstance within a clinical or subject-derived sample, including thederivation of qualitative or quantitative concentration levels of suchsubstances, or otherwise evaluating the values or categorization of asubject's clinical parameters.

“Negative predictive value” or “NPV” is calculated by TN/(TN+FN) or thetrue negative fraction of all negative test results. It also isinherently impacted by the prevalence of the disease and pre-testprobability of the population intended to be tested. See, e.g.,O'Marcaigh A S, Jacobson R M, “Estimating the Predictive Value of aDiagnostic Test: How To Prevent Misleading or Confusing Results,” Clin.Ped. 1993, 32(8):485-491, which discusses specificity, sensitivity, andpositive and negative predictive values of a test, e.g., a clinicaldiagnostic test. Often, for binary disease state classificationapproaches using a continuous diagnostic test measurement, thesensitivity and specificity is summarized by Receiver OperatingCharacteristics (ROC) curves according to Pepe et al., “Limitations ofthe Odds Ratio in Gauging the Performance of a Diagnostic, Prognostic,or Screening Marker,” Am. J. Epidemiol 2004, 159(9):882-890, andsummarized by the Area Under the Curve (AUC) or c-statistic, anindicator that allows representation of the sensitivity and specificityof a test, assay, or method over the entire range of test (or assay) cutpoints with just a single value. See also, e.g., Shultz, “ClinicalInterpretation Of Laboratory Procedures,” chapter 14 in Teitz,Fundamentals of Clinical Chemistry, Burtis and Ashwood (eds.), 4thedition 1996, W.B. Saunders Company, pages 192-199; and Zweig et al.,“ROC Curve Analysis: An Example Showing the Relationships Among SerumLipid and Apolipoprotein Concentrations in Identifying Subjects withCoronory Artery Disease,” Clin. Chem., 1992, 38(8):1425-1428. Analternative approach using likelihood functions, odds ratios,information theory, predictive values, calibration (includinggoodness-of-fit), and reclassification measurements is summarizedaccording to Cook, “Use and Misuse of the Receiver OperatingCharacteristic Curve in Risk Prediction,” Circulation 2007, 115:928-935. Hazard ratios and absolute and relative risk ratios withinsubject cohorts defined by a test are a further measurement of clinicalaccuracy and utility. In this last, multiple methods are frequently usedto defining abnormal or disease values, including reference limits,discrimination limits, and risk thresholds as per Vasan, “Biomarkers ofCardiovascular Disease: Molecular Basis and Practical Considerations,”Circulation 2006, 113:2335-2362.

Analytical accuracy refers to the repeatability and predictability ofthe measurement process itself, and may be summarized in suchmeasurements as coefficients of variation, and tests of concordance andcalibration of the same samples or controls with different times, users,equipment and/or reagents. These and other considerations in evaluatingnew biomarkers are also summarized in Vasan, Circulation 2006,113:2335-2362.

“Normal glucose levels” is used interchangeably with the term“normoglycemic” and “normal” and refers to a fasting venous plasmaglucose concentration of less than 6.1 mmol/L (110 mg/dL). Although thisamount is arbitrary, such values have been observed in subjects withproven normal glucose tolerance, although some may have IGT as measuredby oral glucose tolerance test (OGTT). Glucose levels abovenormoglycemic are considered a pre-diabetic condition.

“Performance” is a term that relates to the overall usefulness andquality of a diagnostic or prognostic test, including, among others,clinical and analytical accuracy, other analytical and processcharacteristics, such as use characteristics (e.g., stability, ease ofuse), health economic value, and relative costs of components of thetest. Any of these factors may be the source of superior performance andthus usefulness of the test.

“Positive predictive value” or “PPV” is calculated by TP/(TP+FP) or thetrue positive fraction of all positive test results. It is inherentlyimpacted by the prevalence of the disease and pre-test probability ofthe population intended to be tested.

“Pre-Diabetes” or “pre-Diabetic,” in the context of the presentinvention, indicates the physiological state, in an individual or in apopulation, and absent any therapeutic intervention (diet, exercise,pharmaceutical, or otherwise) of having a higher than normal expectedrate of disease conversion to frank Type 2 Diabetes Mellitus.Pre-Diabetes can also refer to those subjects or individuals, or apopulation of subjects or individuals who will, or are predicted toconvert to frank Type 2 Diabetes Mellitus within a given time period ortime horizon at a higher rate than that of the general, unselectedpopulation. Such absolute predicted rate of conversion to frank Type 2Diabetes Mellitus in pre-Diabetes populations may be as low as 1 percentor more per annum, but preferably 2 percent per annum or more. It mayalso be stated in terms of a relative risk from normal between quartilesof risk or as a likelihood ratio between differing biomarker and indexscores, including those coming from the invention. Unless otherwisenoted, and without limitation, when a categorical positive diagnosis ofpre-Diabetes is stated here, it is defined experimentally with referenceto the group of subjects with a predicted conversion rate to Type 2Diabetes mellitus of two percent (2%) or greater per annum over thecoming 5 years, or ten percent (10%) or greater in the entire period, ofthose testing at a given threshold value (the selected pre-Diabetesclinical cutoff). When a continuous measure of Diabetes conversion riskis produced, pre-Diabetes encompasses any expected annual rate ofconversion above that seen in a normal reference or general unselectednormal prevalence population. When a complete study is retrospectivelydiscussed in the Examples, pre-Diabetes encompasses the baselinecondition of all of the “Converters” or “Cases” arms, each of whomconverted to Type 2 Diabetes Mellitus during the study.

In an unselected individual population, pre-Diabetes overlaps with, butis not necessarily a complete superset of, or contained subset within,all those with “pre-diabetic conditions;” as many who will convert toDiabetes in a given time horizon are now apparently healthy, and with noobvious pre-diabetic condition, and many have pre-diabetic conditionsbut will not convert in a given time horizon; such is the diagnostic gapand need to be fulfilled by the invention. Taken as a population,individuals with pre-Diabetes have a predictable risk of conversion toDiabetes (absent therapeutic intervention) compared to individualswithout pre-Diabetes and otherwise risk matched.

“Pre-diabetic condition” refers to a metabolic state that isintermediate between normal glucose homeostasis and metabolism andstates seen in frank Diabetes Mellitus. Pre-diabetic conditions include,without limitation, Metabolic Syndrome (“Syndrome X”), Impaired GlucoseTolerance (IGT), and Impaired Fasting Glycemia (IFG). IGT refers topost-prandial abnormalities of glucose regulation, while IFG refers toabnormalities that are measured in a fasting state. The World HealthOrganization defines values for IFG as a fasting plasma glucoseconcentration of 6.1 mmol/L (100 mg/dL) or greater (whole blood 5.6mmol/L; 100 mg/dL), but less than 7.0 mmol/L (126 mg/dL) (whole blood6.1 mmol/L; 110 mg/dL). Metabolic syndrome according to the NationalCholesterol Education Program (NCEP) criteria is defined as having atleast three of the following: blood pressure greater than or equal to130/85 mm Hg; fasting plasma glucose greater than or equal to 6.1mmol/L; waist circumference >102 cm (men) or >88 cm (women);triglycerides greater than or equal to 1.7 mmol/L; and HDL cholesterol<1.0 mmol/L (men) or <1.3 mmol/L (women). Many individuals withpre-diabetic conditions will not convert to T2DM.

“Risk” in the context of the present invention, relates to theprobability that an event will occur over a specific time period, as inthe conversion to frank Diabetes, and can can mean a subject's“absolute” risk or “relative” risk. Absolute risk can be measured withreference to either actual observation post-measurement for the relevanttime cohort, or with reference to index values developed fromstatistically valid historical cohorts that have been followed for therelevant time period. Relative risk refers to the ratio of absoluterisks of a subject compared either to the absolute risks of low-riskcohorts or an average population risk, which can vary by how clinicalrisk factors are assessed. Odds ratios, the proportion of positiveevents to negative events for a given test result, are also commonlyused (odds are according to the formula p/(1−p) where p is theprobability of event and (1−p) is the probability of no event) tono-conversion. Alternative continuous measures which may be assessed inthe context of the present invention include time to Diabetes conversionand therapeutic Diabetes conversion risk reduction ratios.

“Risk evaluation” or “evaluation of risk,” in the context of the presentinvention, encompasses making a prediction of the probability, odds, orlikelihood that an event or disease state may occur, the rate ofoccurrence of the event or conversion from one disease state to another,i.e., from a normoglycemic condition to a pre-diabetic condition orpre-Diabetes, or from a pre-diabetic condition to pre-Diabetes orDiabetes. Risk evaluation can also comprise prediction of futureglucose, HBA1c scores or other indices of Diabetes, either in absoluteor relative terms in reference to a previously measured population. Themethods of the present invention may be used to make continuous orcategorical measurements of the risk of conversion to Type 2 Diabetes,thus diagnosing and defining the risk spectrum of a category of subjectsdefined as pre-diabetic. In the categorical scenario, the invention canbe used to discriminate between normal and pre-Diabetes subject cohorts.In other embodiments, the present invention may be used so as todiscriminate pre-Diabetes from Diabetes, or Diabetes from normal. Suchdiffering use may require different biomarker combinations in individualpanels, mathematical algorithms, and/or cutoff points, but be subject tothe same aforementioned measurements of accuracy for the intended use.

A “sample” in the context of the present invention is a biologicalsample isolated from a subject and can include, by way of example andnot limitation, whole blood, serum, plasma, blood cells, endothelialcells, tissue biopsies, lymphatic fluid, ascites fluid, interstitialfluid (also known as “extracellular fluid” and encompasses the fluidfound in spaces between cells, including, inter alia, gingivalcrevicular fluid), bone marrow, cerebrospinal fluid (CSF), saliva,mucus, sputum, sweat, urine, or any other secretion, excretion, or otherbodily fluids. “Blood sample” refers to whole blood or any fractionthereof, including blood cells, serum and plasma; serum is a preferredblood sample.

“Sensitivity” is calculated by TP/(TP+FN) or the true positive fractionof disease subjects.

“Specificity” is calculated by TN/(TN+FP) or the true negative fractionof non-disease or normal subjects.

By “statistically significant,” it is meant that the alteration isgreater than what might be expected to happen by chance alone (whichcould be a “false positive”). Statistical significance can be determinedby any method known in the art. Commonly used measures of significanceinclude the p-value, which presents the probability of obtaining aresult at least as extreme as a given data point, assuming the datapoint was the result of chance alone. A result is often consideredhighly significant at a p-value of 0.05 or less.

A “subject” in the context of the present invention is preferably amammal. The mammal can be a human, non-human primate, mouse, rat, dog,cat, horse, or cow, but are not limited to these examples. Mammals otherthan humans can be advantageously used as subjects that represent animalmodels of Diabetes Mellitus, pre-Diabetes, or pre-diabetic conditions. Asubject can be male or female. A subject can be one who has beenpreviously diagnosed or identified as having Diabetes, pre-Diabetes, ora pre-diabetic condition, and optionally has already undergone, or isundergoing, a therapeutic intervention for the Diabetes, pre-Diabetes,or pre-diabetic condition. Alternatively, a subject can also be one whohas not been previously diagnosed as having Diabetes, pre-Diabetes, or apre-diabetic condition. For example, a subject can be one who exhibitsone or more risk factors for Diabetes, pre-Diabetes, or a pre-diabeticcondition, or a subject who does not exhibit Diabetes risk factors, or asubject who is asymptomatic for Diabetes, pre-Diabetes, or pre-diabeticconditions. A subject can also be one who is suffering from or at riskof developing Diabetes, pre-Diabetes, or a pre-diabetic condition.

“TN” is true negative, which for a disease state test means classifyinga non-disease or normal subject correctly.

“TP” is true positive, which for a disease state test means correctlyclassifying a disease subject.

“Traditional laboratory risk factors” or “TLRFs” correspond tobiomarkers isolated or derived from subject samples and which arecurrently evaluated in the clinical laboratory and used in traditionalglobal risk assessment algorithms, such as Stern, Framingham, FinlandDiabetes Risk Score, ARIC Diabetes, and Archimedes. Traditionallaboratory risk factors commonly tested from subject blood samplesinclude, but are not limited to, total cholesterol (CHOL), LDL(LDL/LDLC), HDL (HDL/HDLC), VLDL (VLDLC), triglycerides (TRIG), glucose(including, without limitation, the fasting plasma glucose (Glucose) andthe oral glucose tolerance test (OGTT)) and HBA1c (HBA1C) levels.

Again, where aspects or embodiments of the invention are describedherein in terms of a Markush group or other grouping of alternatives,the present invention encompasses not only the entire group listed as awhole, but each member of the group individually and all possiblesubgroups of the main group, but also the main group absent one or moreof the group members. The present invention also envisages the explicitexclusion of one or more of any of the group members in the claimedinvention.

Quantitative Surrogate Markers for Diabetic Conditions

In some embodiments, the invention provides a method of assessing adiabetic condition. In some embodiments, the assessment of the diabeticcondition comprises diagnosing, classifying, identifying, monitoring,determining the likelihood of risk of developing, determining the degree(or severity), and/or assessing the progression and/or regression of thediabetic condition. In some embodiments, the diabetic condition is aprediabetic condition. In some embodiments, the diabetic condition isinsulin resistance. In some embodiments, the diabetic condition isimpaired glucose tolerance. (The term “impaired glucose tolerance” isused interchangeably herein with “glucose intolerance.”) In someembodiments, the diabetic condition is impaired fasting glucose. In someembodiments, the diabetic condition is prediabetes. In some embodiments,the diabetic condition is a form of diabetes.

In some embodiments, the invention provides testing methods that can beused to diagnose, classify, and/or monitor patients with diabeticconditions wherein the condition is selected from the group consistingof: diabetes, Type 2 diabetes, insulin resistance, impaired glucosetolerance, impaired fasting glucose, prediabetes, metabolic syndrome,hepatic steatosis, insulin sensitivity, hyperinsulinemia, hepaticsteatosis, muscle steatosis, hyperlipidemia, hypercholesterolemia. Insome embodiments, the invention provides testing methods that can beused to diagnose, classify, and/or monitor patients with diabeticconditions wherein the condition is selected from the group consistingof: oral glucose intolerance, insulin resistance, insulin sensitivity,hepatic steatosis, Type 2 diabetes, and gestational diabetes. In someembodiments, the invention provides testing methods that can be used todiagnose, classify, and/or monitor patients with diabetic conditionswherein the condition is oral glucose intolerance or insulin resistance.In some further embodiments, the invention provides testing methods thatcan be used to diagnose, classify, and/or monitor patients with diabeticconditions wherein the condition is selected from the group consistingof: non-alcoholic steatohepatitis (NASH), pediatric NASH, obesity,childhood obesity, metabolic syndrome, and polycystic ovary disease.

Diabetes and its related comorbidities and conditions are largely due tochanges in the metabolism of lipids. The inventors have discovered thatparticular amounts of specific lipid metabolites in body fluidscorrelate with the diabetic condition.

In some aspects, the invention provides metabolic markers for oralglucose intolerance. Impaired glucose tolerance and impaired fastingglucose are known to be pre-diabetic states (Lin et al., Tohoku J. Exp.Med., 212:349-57 (2007)). Impaired oral glucose tolerance has beenreported as being a predictor of non-alcoholic fatty liver disease inobese children (Sartorio et al., Eur. J. Clin. Nutr., 61:877-83 (2007))and of steatoheptatitis and fibrosis in patients with non-alcoholicfatty liver disease (Haukeland et al., Scand. J. Gastroenterol.40:1469-77 (2005)). The use of oral glucose tolerance testing (OGTT) fordetection of gestational diabetes has also been reported (Lapolla etal., J. Clin. Endocrinol. Metab., 2007 Dec. 18 [Epub ahead of print]).In addition, oral glucose intolerance and insulin sensitivity have beenlinked to Polycystic Ovary Syndrome (Amato et al., Clin Endocrinol.(Oxf), 2007 Nov. 22 [Epub ahead of print]).

In some embodiments, the markers of the invention are used as asubstitute for an existing test used to assess a diabetic condition(e.g., fasting blood glucose level or oral glucose tolerance test(OGTT)). In other embodiments, the markers of the invention are used ina test to identify or select a subject for further testing for thediabetic condition via another method including, but not limited tofasting blood glucose level or OGTT.

Diagnostic and Prognostic Indications of the Invention

The invention provides improved diagnosis and prognosis of Diabetes,pre-Diabetes, or a pre-diabetic condition. The risk of developingDiabetes, pre-Diabetes, or a pre-diabetic condition can be detected witha pre-determined level of predictability by measuring various biomarkerssuch as including, but not limited to, proteins, nucleic acids,polymorphisms, lipid metabolites, and other analytes in a test samplefrom a subject, and comparing the measured values to reference or indexvalues, often utilizing mathematical algorithms or formulas in order tocombine information from results of multiple individual biomarkers andfrom non-analyte clinical parameters into a single measurement or index.Subjects identified as having an increased risk of Diabetes,pre-Diabetes, or a pre-diabetic condition can optionally be selected toreceive treatment regimens, such as administration of prophylactic ortherapeutic compounds such as “Diabetes-modulating agents” as definedherein, or implementation of exercise regimens or dietary supplements toprevent or delay the onset of Diabetes, pre-Diabetes, or a pre-diabeticcondition.

The amount of the biomarker can be measured in a test sample andcompared to the “normal control level,” utilizing techniques such asreference limits, discrimination limits, or risk defining thresholds todefine cutoff points and abnormal values for Diabetes, pre-Diabetes, andpre-diabetic conditions, all as described in Vasan, 2006. The normalcontrol level means the level of one or more biomarkers or combinedbiomarker indices typically found in a subject not suffering fromDiabetes, pre-Diabetes, or a pre-diabetic condition. Such normal controllevel and cutoff points may vary based on whether a biomarker is usedalone or in a formula combining with other biomarkers into an index.Alternatively, the normal control level can be a database of biomarkerpatterns from previously tested subjects who did not convert to Diabetesover a clinically relevant time horizon.

The present invention may be used to make continuous or categoricalmeasurements of the risk of conversion to Type 2 Diabetes, thusdiagnosing and defining the risk spectrum of a category of subjectsdefined as pre-diabetic. In the categorical scenario, the methods of thepresent invention can be used to discriminate between normal andpre-Diabetes subject cohorts. In other embodiments, the presentinvention may be used so as to discriminate pre-Diabetes from Diabetes,or Diabetes from normal. Such differing use may require differentbiomarker combinations in individual panels, mathematical algorithms,and/or cutoff points, but subject to the same aforementionedmeasurements of accuracy for the intended use.

Identifying the pre-diabetic subject enables the selection andinitiation of various therapeutic interventions or treatment regimens inorder to delay, reduce or prevent that subject's conversion to a frankDiabetes disease state. Levels of an effective amount of biomarkers alsoallow for the course of treatment of Diabetes, pre-Diabetes or apre-diabetic condition to be monitored. In this method, a biologicalsample can be provided from a subject undergoing treatment regimens ortherapeutic interventions, e.g., drug treatments, for Diabetes. Suchtreatment regimens or therapeutic interventions can include, but are notlimited to, exercise regimens, dietary modification, dietarysupplementation, bariatric surgical intervention, administration ofpharmaceuticals, and treatment with therapeutics or prophylactics usedin subjects diagnosed or identified with Diabetes, pre-Diabetes, or apre-diabetic condition. If desired, biological samples are obtained fromthe subject at various time points before, during, or after treatment.

The present invention can also be used to screen patient or subjectpopulations in any number of settings. For example, a health maintenanceorganization, public health entity or school health program can screen agroup of subjects to identify those requiring interventions, asdescribed above, or to collect epidemiological data. Insurance companies(e.g., health, life, or disability) may screen applicants in the processof determining coverage or pricing, or existing clients for possibleintervention. Data collected in such population screens, particularlywhen tied to any clinical progression to conditions like Diabetes,pre-Diabetes, or a pre-diabetic condition, will be of value in theoperations of, for example, health maintenance organizations, publichealth programs and insurance companies. Such data arrays or collectionscan be stored in machine-readable media and used in any number ofhealth-related data management systems to provide improved healthcareservices, cost effective healthcare, improved insurance operation, etc.See, for example, U.S. Patent Application No.; U.S. Patent ApplicationNo. 2002/0038227; U.S. Patent Application No. US 2004/0122296; U.S.Patent Application No. US 2004/0122297; and U.S. Pat. No. 5,018,067.Such systems can access the data directly from internal data storage orremotely from one or more data storage sites as further detailed herein.Thus, in a health-related data management system, wherein risk ofdeveloping a diabetic condition for a subject or a population comprisesanalyzing Diabetes risk factors, the present invention provides animprovement comprising use of a data array encompassing the biomarkermeasurements as defined herein and/or the resulting evaluation of riskfrom those biomarker measurements.

A machine-readable storage medium can comprise a data storage materialencoded with machine-readable data or data arrays which, when using amachine programmed with instructions for using said data, is capable ofuse for a variety of purposes, such as, without limitation, subjectinformation relating to Diabetes risk factors over time or in responseto Diabetes-modulating drug therapies, drug discovery, and the like.Measurements of effective amounts of the biomarkers of the inventionand/or the resulting evaluation of risk from those biomarkers canimplemented in computer programs executing on programmable computers,comprising, inter alia, a processor, a data storage system (includingvolatile and non-volatile memory and/or storage elements), at least oneinput device, and at least one output device. Program code can beapplied to input data to perform the functions described above andgenerate output information. The output information can be applied toone or more output devices, according to methods known in the art. Thecomputer may be, for example, a personal computer, microcomputer, orworkstation of conventional design.

Each program can be implemented in a high-level procedural orobject-oriented programming language to communicate with a computersystem. However, the programs can be implemented in assembly or machinelanguage, if desired. The language can be a compiled or interpretedlanguage. Each such computer program can be stored on a storage media ordevice (e.g., ROM or magnetic diskette or others as defined elsewhere inthis disclosure) readable by a general or special-purpose programmablecomputer, for configuring and operating the computer when the storagemedia or device is read by the computer to perform the proceduresdescribed herein. The health-related data management system of theinvention may also be considered to be implemented as acomputer-readable storage medium, configured with a computer program,where the storage medium so configured causes a computer to operate in aspecific and predefined manner to perform various functions describedherein. Levels of an effective amount of biomarkers can then bedetermined and compared to a reference value, e.g. a control subject orpopulation whose diabetic state is known or an index value or baselinevalue. The reference sample or index value or baseline value may betaken or derived from one or more subjects who have been exposed to thetreatment, or may be taken or derived from one or more subjects who areat low risk of developing Diabetes, pre-Diabetes, or a pre-diabeticcondition, or may be taken or derived from subjects who have shownimprovements in Diabetes risk factors (such as clinical parameters ortraditional laboratory risk factors as defined herein) as a result ofexposure to treatment. Alternatively, the reference sample or indexvalue or baseline value may be taken or derived from one or moresubjects who have not been exposed to the treatment. For example,samples may be collected from subjects who have received initialtreatment for Diabetes, pre-Diabetes, or a pre-diabetic condition andsubsequent treatment for Diabetes, pre-Diabetes, or a pre-diabeticcondition to monitor the progress of the treatment. A reference valuecan also comprise a value derived from risk prediction algorithms orcomputed indices from population studies such as those disclosed herein.

FIG. 6 illustrates an example of a suitable computing system environment100 on which a system for the steps of the claimed method and apparatusmay be implemented. The computing system environment 100 is only oneexample of a suitable computing environment and is not intended tosuggest any limitation as to the scope of use or functionality of themethod of apparatus of the claims. Neither should the computingenvironment 100 be interpreted as having any dependency or requirementrelating to any one or combination of components illustrated in theexemplary operating environment 100.

The steps of the claimed method and system are operational with numerousother general purpose or special purpose computing system environmentsor configurations. Examples of well known computing systems,environments, and/or configurations that may be suitable for use withthe methods or system of the claims include, but are not limited to,personal computers, server computers, hand-held or laptop devices,multiprocessor systems, microprocessor-based systems, set-top boxes,programmable consumer electronics, network PCs, minicomputers, mainframecomputers, distributed computing environments that include any of theabove systems or devices, and the like, including those systems,environments, configurations and means described elsewhere within thisdisclosure.

The steps of the claimed method and system may be described in thegeneral context of computer-executable instructions, such as programmodules, being executed by a computer. Generally, program modulesinclude routines, programs, objects, components, data structures, etc.that perform particular tasks or implement particular abstract datatypes. The methods and apparatus may also be practiced in distributedcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In bothintegrated and distributed computing environments, program modules maybe located in both local and remote computer storage media includingmemory storage devices.

With reference to FIG. 6, an exemplary system for implementing the stepsof the claimed method and system includes a general purpose computingdevice in the form of a computer 110. Components of computer 110 mayinclude, but are not limited to, a processing unit 120, a system memory130, and a system bus 121 that couples various system componentsincluding the system memory to the processing unit 120. The system bus121 may be any of several types of bus structures including a memory busor memory controller, a peripheral bus, and a local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) (bus also known as Mezzanine bus).

The computer 110 typically includes a variety of computer readablemedia. Computer-readable media can be any available media that can beaccessed by computer 110 and includes both volatile and nonvolatilemedia, removable and non-removable media. By way of example, and notlimitation, computer-readable media may comprise computer storage mediaand communication media. Computer storage media includes both volatileand nonvolatile, removable and non-removable media implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules orother data. Computer storage media includes, but is not limited to, RAM,ROM, EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can accessed by the computer 110. Communication mediatypically embodies computer-readable instructions, data structures,program modules or other data in a modulated data signal such as acarrier wave or other transport mechanism and includes any informationdelivery media. The term “modulated data signal” means a signal that hasone or more of its characteristics set or changed in such a manner as toencode information in the signal. By way of example, and not limitation,communication media includes wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared and other wireless media. Combinations of the any of the aboveshould also be included within the scope of computer-readable media.

The system memory 130 includes computer storage media in the form ofvolatile and/or nonvolatile memory such as read-only memory (ROM) 131and random access memory (RAM) 132. A basic input/output system 133(BIOS), containing the basic routines that help to transfer informationbetween elements within the computer 110, such as during start-up, istypically stored in ROM 131. RAM 132 typically contains data and/orprogram modules that are immediately accessible to and/or presentlybeing operated on by the processing unit 120. By way of example, and notlimitation, FIG. 6 illustrates an operating system 134, applicationprograms 135, other program modules 136, and program data 137.

The computer 110 may also include other removable/non-removable,volatile/nonvolatile computer storage media. By way of example only,FIG. 6 illustrates a hard disk drive 140 that reads from or writes tonon-removable, nonvolatile magnetic media, a magnetic disk drive 151that reads from or writes to a removable, nonvolatile magnetic disk 152,and an optical disk drive 155 that reads from or writes to a removable,nonvolatile optical disk 156 such as a CD-ROM or other optical media.Other removable/non-removable, volatile/nonvolatile computer storagemedia that can be used in the exemplary operating environment include,but are not limited to, magnetic tape cassettes, flash memory cards,digital versatile disks, digital video tape, solid state RAM, solidstate ROM, and the like. The hard disk drive 141 is typically connectedto the system bus 121 through a non-removable memory interface such asinterface 140, and magnetic disk drive 151 and optical disk drive 155are typically connected to the system bus 121 by a removable memoryinterface, such as interface 150.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 6 provide storage of computer-readableinstructions, data structures, program modules and other data for thecomputer 110. In FIG. 6, for example, the hard disk drive 141 isillustrated as storing operating system 144, application programs 145,other program modules 146, and program data 147. Note that thesecomponents can either be the same as or different from the operatingsystem 134, application programs 135, other program modules 136, andprogram data 137. The operating system 144, application programs 145,other program modules 146, and program data 147 are given differentnumbers here to illustrate that, at a minimum, they are differentcopies. A user may enter commands and information into the computer 20through input devices such as a keyboard 162 and pointing device 161,commonly referred to as a mouse, trackball, or touch pad. Other inputdevices (not shown) may include a microphone, joystick, game pad,satellite dish, scanner, or the like. These and other input devices areoften connected to the processing unit 120 through a user inputinterface 160 that is coupled to the system bus, but may be connected byother interface and bus structures, such as a parallel port, game portor a universal serial bus (USB). A monitor 191 or other type of displaydevice is also connected to the system bus 121 via an interface, such asa video interface 190. In addition to the monitor, computers may alsoinclude other peripheral output devices such as speakers 197 and printer196, which may be connected through an output peripheral interface 190.

The biomarkers of the present invention can thus be used to generate a“reference biomarker profile” of those subjects who do not haveDiabetes, pre-Diabetes, or a pre-diabetic condition such as impairedglucose tolerance, and would not be expected to develop Diabetes,pre-Diabetes, or a pre-diabetic condition. The biomarkers disclosedherein can also be used to generate a “subject biomarker profile” takenfrom subjects who have Diabetes, pre-Diabetes, or a pre-diabeticcondition like impaired glucose tolerance. The subject biomarkerprofiles can be compared to a reference biomarker profile to diagnose oridentify subjects at risk for developing Diabetes, pre-Diabetes or apre-diabetic condition, to monitor the progression of disease, as wellas the rate of progression of disease, and to monitor the effectivenessof Diabetes, pre-Diabetes or pre-diabetic condition treatmentmodalities. The reference and subject biomarker profiles of the presentinvention can be contained in a machine-readable medium, such as but notlimited to, analog tapes like those readable by a VCR, CD-ROM, DVD-ROM,USB flash media, among others. Such machine-readable media can alsocontain additional test results, such as, without limitation,measurements of clinical parameters and traditional laboratory riskfactors. Alternatively or additionally, the machine-readable media canalso comprise subject information such as medical history and anyrelevant family history. The machine readable media can also containinformation relating to other Diabetes-risk algorithms and computedindices such as those described herein.

Differences in the genetic makeup of subjects can result in differencesin their relative abilities to metabolize various drugs, which maymodulate the symptoms or risk factors of Diabetes, pre-Diabetes or apre-diabetic condition. Subjects that have Diabetes, pre-Diabetes, or apre-diabetic condition, or at risk for developing Diabetes,pre-Diabetes, or a pre-diabetic condition can vary in age, ethnicity,body mass index (BMI), total cholesterol levels, blood glucose levels,blood pressure, LDL and HDL levels, and other parameters. Accordingly,use of the biomarkers disclosed herein, both alone and together incombination with known genetic factors for drug metabolism, allow for apre-determined level of predictability that a putative therapeutic orprophylactic to be tested in a selected subject will be suitable fortreating or preventing Diabetes, pre-Diabetes, or a pre-diabeticcondition in the subject.

To identify therapeutics or drugs that are appropriate for a specificsubject, a test sample from the subject can also be exposed to atherapeutic agent or a drug, and the level of one or more biomarkers canbe determined. The level of one or more biomarkers can be compared tosample derived from the subject before and after treatment or exposureto a therapeutic agent or a drug, or can be compared to samples derivedfrom one or more subjects who have shown improvements in Diabetes orpre-Diabetes risk factors (e.g., clinical parameters or traditionallaboratory risk factors) as a result of such treatment or exposure.

Agents for reducing the risk of Diabetes, pre-Diabetes, pre-diabeticconditions, or diabetic complications include, without limitation of thefollowing, insulin, hypoglycemic agents, anti-inflammatory agents, lipidreducing agents, anti-hypertensives such as calcium channel blockers,beta-adrenergic receptor blockers, cyclooxygenase-2 inhibitors,angiotensin system inhibitors, ACE inhibitors, renin inhibitors,together with other common risk factor modifying agents (herein“Diabetes-modulating drugs”).

The term “insulin (INS)” includes mature insulin (insulin-M),pro-insulin and soluble c-peptide (SCp). “Insulin” includes rapid-actingforms, such as Insulin lispro rDNA origin: HUMALOG (1.5 mL, 10 mL, EliLilly and Company, Indianapolis, Ind.), Insulin Injection (RegularInsulin) form beef and pork (regular ILETIN I, Eli Lilly], human: rDNA:HUMULIN R (Eli Lilly), NOVOLIN R (Novo Nordisk, New York, N.Y.),Semisynthetic: VELOSULIN Human (Novo Nordisk), rDNA Human, Buffered:VELOSULIN BR, pork: regular Insulin (Novo Nordisk), purified pork: PorkRegular ILETIN II (Eli Lilly), Regular Purified Pork Insulin (NovoNordisk), and Regular (Concentrated) ILETIN II U-500 (500 units/mL, EliLilly); intermediate-acting forms such as Insulin Zinc Suspension, beefand pork: LENTE ILETIN G I (Eli Lilly), Human, rDNA: HUMULIN L (EliLilly), NOVOLIN L (Novo Nordisk), purified pork: LENTE ILETIN II (EliLilly), Isophane Insulin Suspension (NPH): beef and pork: NPH ILETIN I(Eli Lilly), Human, rDNA: HUMULIN N (Eli Lilly), Novolin N (NovoNordisk), purified pork: Pork NPH Iletin II (Eli Lilly), NPH-N(NovoNordisk); and long-acting forms such as Insulin zinc suspension,extended (ULTRALENTE, Eli Lilly), human, rDNA: HUMULIN U (Eli Lilly).

A subject cell (i.e., a cell isolated from a subject) can be incubatedin the presence of a candidate agent and the pattern of biomarkerexpression in the test sample is measured and compared to a referenceprofile, e.g., a Diabetes reference expression profile or a non-Diabetesreference expression profile or an index value or baseline value. Thetest agent can be any compound or composition or combination thereof.For example, the test agents are agents frequently used in Diabetestreatment regimens and are described herein.

Additionally, any of the aforementioned methods can be used separatelyor in combination to assess if a subject has shown an “improvement inDiabetes risk factors” or moved within the risk spectrum ofpre-Diabetes. Such improvements include, without limitation, a reductionin body mass index (BMI), a reduction in blood glucose levels, anincrease in HDL levels, a reduction in systolic and/or diastolic bloodpressure, an increase in insulin levels, or combinations thereof.

A subject suffering from or at risk of developing Diabetes or apre-diabetic condition may also be suffering from or at risk ofdeveloping arteriovascular disease, hypertension, or obesity. Type 2Diabetes in particular and arteriovascular disease have many riskfactors in common, and many of these risk factors are highly correlatedwith one another. The relationship among these risk factors may beattributable to a small number of physiological phenomena, perhaps evena single phenomenon. Subjects suffering from or at risk of developingDiabetes, arteriovascular disease, hypertension or obesity areidentified by methods known in the art.

Because of the interrelationship between Diabetes and arteriovasculardisease, some or all of the individual biomarkers and biomarker panelsof the present invention may overlap or be encompassed by biomarkers ofarteriovascular disease, and indeed may be useful in the diagnosis ofthe risk of arteriovascular disease.

Mole Percentage Fatty Acid Compositions as Surrogates for DiabeticConditions

A lipid metabolite that is a relative proportion of a triglyceride (orany other lipid class) can be measured in a body fluid, such as serum orplasma, as a quantitative measure of the relative proportion of thatlipid metabolite in hepatic triglycerides (or other lipid class). Ifthis relative proportion of lipid metabolite (or a collection of lipidmetabolites) correlates with insulin resistance, it serves as aquantitative surrogate of the insulin resistance. Thus, the molepercentage of a particular fatty acid within a particular lipid classmay be used as a quantitative surrogate for insulin resistance.

In one embodiment, the mole percentage of a single lipid metabolite maybe used in the methods of the invention. In other embodiments, molepercentages of two or more lipid metabolites may be used in the methodsof the invention, for example, 2, 3, 4, 5, 10, 15, 20, or more lipidmetabolites.

According to the present invention, when analyzing the effects renderedby two or more lipid metabolites, one can either evaluate the effects ofthese lipid metabolites individually or obtain the net effect of theselipid metabolites, e.g., by using various mathematical formulas ormodels to quantify the effect of each lipid metabolite. A formulacontaining the levels of one or more lipid metabolites as variablesincludes any mathematical formula, model, equation, or expressionestablished based on mathematic or statistical principles or methodsusing the values of one or more lipid metabolites as variables. Asdescribed herein, mathematical formulas or models can be used toevaluate and weigh the effects of lipid metabolism in combination withprotein biomarkers and/or other factors.

In general, any suitable mathematic analyses can be used to analyze thenet effect of two or more lipid metabolites (or one or more lipidmetabolites in combination with protein biomarkers or other factors)with respect to projecting the diabetic condition of a subject. Forexample, methods such as multivariate analysis of variance, multivariateregression, and multiple regression can be used to determinerelationships between dependent variables and independent variables.Clustering, including both hierarchical and nonhierarchical methods, aswell as nonmetric Dimensional Scaling can be used to determineassociations among variables and among changes in those variables.

In addition, principal component analysis is a common way of reducingthe dimension of studies, and can be used to interpret thevariance-covariance structure of a data set. Principal components may beused in such applications as multiple regression and cluster analysis.Factor analysis is used to describe the covariance by constructing“hidden” variables from the observed variables. Factor analysis may beconsidered an extension of principal component analysis, where principalcomponent analysis is used as parameter estimation along with themaximum likelihood method. Furthermore, simple hypothesis such asequality of two vectors of means can be tested using Hotelling's Tsquared statistic.

In one embodiment, a formula containing one or more lipid metabolites(optionally in combination with one or more protein biomarkers or otherfactors) as variables is established by using regression analyses, e.g.,multiple linear regressions. Examples of formulas developed include,without any limitation, the following:

k+k ₁(FA ₁)+k ₂(FA ₂)+k ₃(FA ₃)  Formula I:

k−k ₁(FA ₁)+k ₂(FA ₂)+k ₃(FA ₃)  Formula II:

k+k ₁(FA ₁)−k ₂(FA ₂)+k ₃(FA ₃)  Formula III:

k+k ₁(FA ₁)+k ₂(FA ₂)−k ₃(FA ₃)  Formula IV:

k−k ₁(FA ₁)−k ₂(FA ₂)+k ₃(FA ₃)  Formula V:

k+k ₁(FA ₁)−k ₂(FA ₂)−k ₃(FA ₃)  Formula VI:

k−k ₁(FA ₁)+k ₂(FA ₂)−k ₃(FA ₃)  Formula VII:

k−k ₁(FA ₁)−k ₂(FA ₂)−k ₃(FA ₃)  Formula VIII:

The formulas may use one or more lipid metabolites (optionally incombination with one or more protein biomarkers or other factors) asvariables, such as 1, 2, 3, 4, 5, 10, 15, 20, or more lipid metabolites.The constants of these formulas can be established by using a set ofdata obtained from known diabetic conditions. Usually the levels oflipid metabolites used in these formulas can be either the levels at atime point or changes of levels over a period of time.

According to the invention, mathematic formulas established using lipidmetabolites can be used to either qualitatively or quantitatively assessthe diabetic condition of a subject over a period of time. For example,a formula having one or more lipid metabolites as variables can be usedto directly calculate the diabetic condition of a subject. In addition,the net value of a formula containing one or more lipid metabolites canbe compared to the standard value of such formula corresponding to adiabetic condition pattern, e.g. progression or regression of a diabeticcondition, and the results of such comparison can be used to projectdiabetic condition development. Specifically, a subject having a netvalue of a formula similar to or within the range of the standard valueof such formula that is assigned to or associated with a progression ofa diabetic condition is likely to experience a progression over a periodof time. Similarly, a subject having a net value of a formula similar toor within the range of the standard values of such formula that isassigned to or associated with a regression is likely to experience aregression of their diabetic condition over a period of time.

Additional Quantitative Surrogates for Diabetic Condition

Models of lipid metabolites other than mole percentage may be used assurrogate markers for a diabetic condition. For example see the list ofadditional biomarkers, e.g., eicosanoids.

Lipid Metabolites and Additional Biomarkers for Diabetic Conditions

In some embodiments, one or more lipid metabolites are used asmetabolite markers for assessing diabetic conditions. In some otherembodiments, the metabolite markers used comprise both lipid metabolitesand additional biomarkers.

In one embodiment, lipid metabolites include a fatty acid present withina particular lipid class. In one embodiment, the lipid class is selectedfrom the group consisting of neutral lipids, phospholipids, free fattyacids, total fatty acids, triglycerides, cholesterol esters,phosphatidylcholines, and phosphatidylethanolamines. In one embodiment,the lipid class is free fatty acids. In one embodiment, the lipid classis total fatty acids. In one embodiment, the lipid class istriglycerides. In one embodiment, the lipid class is cholesterol esters.In one embodiment, the lipid class is phosphatidylcholines. In oneembodiment, the lipid class is phosphatidylethanolamines. In oneembodiment, the lipid metabolite is selected from the fatty acids shownin Table 3. The method may involve measuring the amount of more than onelipid metabolite, such as 2, 3, 4, 5, 10, 15, 20, or more lipidmetabolites. In one embodiment, two or more lipid metabolites in Table 3are measured. In one embodiment, three or more lipid metabolites inTable 3 are measured.

In one embodiment, the lipid metabolite is positively correlated with adiabetic condition. In one embodiment, the lipid metabolite isnegatively correlated with diabetic condition. In one embodiment, thelipid metabolite is measured as a relative amount within that particularlipid class. In one embodiment, the lipid metabolite is measured in ablood-based body fluid, such as blood, plasma, serum, or lipoproteinfractions.

TABLE 3 Blood-based Lipid Metabolite Markers of Diabetic ConditionCE14.0 FA16.0 PC14.0 PE16.0 TG20.2n6 CE16.0 FA16.1n7 PC16.1n7 PE20.0TG20.3n6 CE20.0 FA18.0 PC18.0 PE16.1n7 TG20.3n9 CE16:1n7 FA18.1n9 PC15.0PE18.1n9 TG22.2n6 CE18.1n7 FA18.1n7 PC18.1n7 PE18:3n6 TG22:4n6 CE18.1n9FA18.2n6 PC18.1n9 PE20.0 CETotal.LC CE18.2n6 FA20.4n6 PC18.2n6 PE20.1n9TGTotal.LC CE18.3n6 FA22.2n6 PC18.3n6 PE20:3n9 DGTotal.LC CE22:2n6FA22.4n6 PC18.3n3 PE20:3n6 FSTotal.LC CE20.3n9 FA20.5n3 PC20.1n9PE20.4n6 AC6:0 CE22.5n6 FA22.6n3 PC20:3n9 PE20.5n3 AC16:0 DG16:0FA24.1n9 PC20:4n3 PEdm16.0 AC14:0 DG18.0 LY18.0 PC20.2n6 PEdm18.0 AC8:0DG18.2n6 LY16.1n7 PC20.4n6 TG14.0 AC10:0 DG18.3n9 LY18.1n7 PC22.4n6TG14.1n5 AC3:0 DG20:0 LY18.1n9 PC22.5n3 TG16.0 AC12:0 DG20.3n6 LY20.3n9PCdm16.0 TG20.0 L-Carnitine DG20.3n9 LY18.2n6 PCdm18.0 TG16.1n7 AC4:0DG22.1n9 LY20:3n6 PCdm18.1n9 TG18.1n7 CE20:3n6 FA14.0 LY22:4n6PCdm18:1n7 TG18.1n9 PE18:1n9 FA15.0 LY22:5n3 PE14.0 TG18.2n6 TG18:0TG18:3n3 TG18:3n3 FA18:1n7 PE22:0 CE18:0 CE20:4n3 TGTL DG18:1n9 DG18:1n9DG18:3n3 DGTL FA16:0 FA16:1n7

In some embodiments, the marker(s) for the diabetic condition(s)comprise one or more, two or more, three or more, four or more, five ormore, or six or more markers selected from the group consisting ofCE16:1n7, CE20:3n6, CE18:2n6, CE16:0, CE18:1n9, LY18:2n6, LY18:1n7 andLY18:1n9.

The following additional biomarkers may aid the diagnosis of diabeticconditions: (1) malonyl-CoA and malonylcarnitine; (2) free carnitine,and acylcamitines listed in Table 4; and (3) sterols and bile acidslisted in Table 5. Body fluid and cellular samples may be used tomeasure these additional biomarkers. Examples of cellular samplesinclude, but are not limited to, lymphocytes and macrophages.

TABLE 4 List of Acylcarnitine Metabolites L-Carnitine ButyrobetaineAcetyl carnitine Propionyl carnitine Butyryl carnitine Hexanoylcarnitine Valeryl carnitine Octanoyl carnitine Decanoyl carnitineMyristoyl carnitine Palmitoyl carnitine Stearoyl carnitine Oleoylcarnitine Linoleoyl carnitine Arachidoyl carnitine Dodecanoyl carnitine

TABLE 5 List of Bile Acid and Sterol Metabolites Cholic AcidChenodeoxycholic Acid Deoxycholic Acid Lithocholic Acid Glycocholic AcidTaurodeoxycholate Glycochenode- Taurochenodeoxycholate β-Muricholic Acidoxycholate Ursodeoxycholic acid Taurodeoxycholic acid Taurolithocholicacid Glycodesoxycholic acid Glycolithocholic acid Taurocholic acidCholesterol Coprostanol Glycoursodeoxycholic Lanosterol Lathosterol acidDesmosterol Campesterol Cholestanol Lathosterol CampesterolBeta-Sitosterol 4-Cholesten-3-One Fucosterol Coprosterol Stigmasterol

Additionally, the following additional biomarkers may aid in thediagnosis of a diabetic condition: (1) The sterols and bile acids listedin Table 5 (levels increase with increased cholesterol synthesis); (2)Eicosanoids including, but not limited to, those shown in Table 6;and/or (3) Cytokines and chemokines including, but not limited to, TNF,IL-6, leptin, and adiponectin. Body fluid and cellular samples may beused to measure the additional markers. Examples of cellular samplesinclude, but are not limited to, lymphocytes and macrophages.

TABLE 6 List of Eicosanoid Metabolites 13-14-dihydro-15-keto PGA2 PGB2PGD2 PGE2 6-keto PGF1a PGF2a 11b-PGF2a 15-keto PGF2a PGJ215-deoxy-o-12,14-PGJ2 TXB2 11-dehydro TXB2 8-iso-PGF2a 9-HODE 13-HODE5-HETE 8-HETE 9-HETE 11-HETE 12-HETE 15-HETE 5(S)-HEPE 12(S)-HEPE15(S)-HEPE LTB4 LTB5 LTC4 LTD4 LTE4 LTF4 Lipoxin A4 20-HETE12(13)-DiHOME 12(13)-EpOME 9(10)-EpOME 5(6)-EpETrE 11l(12)-EpETrE14(15)-EpETrE 5,6-DiHETrE 8,9-DiHETrE 11,12-DiHETrE 14,15-DiHETrE14,15-DiHETE 17,18-DiHETE 14(15)-EpETE 17(18)-EpETE 19(20)-DiHDPA

Measurements of the amounts of one or more of these additionalbiomarkers may be used in the methods of the invention, in addition tomeasurement of a lipid metabolite. In one embodiment, the amount of oneof the biomarkers is measured in a sample from the subject. In oneembodiment, the amounts of two of the biomarkers are measured in asample from the subject. In other embodiments, 3, 4, 5, 6, 7, 8, 10, 12,15, 20, or more of the biomarkers may be measured in a sample from thesubject.

Diagnostic Cutoff Values for Selected Markers Associated with OralGlucose Intolerance:

The concentration of AC6:0 expected to provide utility for diagnosingpre-diabetes and other diabetes-related conditions is between 0.44 and0.70 nMoles per gram of plasma or serum. A higher value is associatedwith a more pronounced diabetic state or increased risk.

The concentration of AC8:0 expected to provide utility for diagnosingpre-diabetes and other diabetes-related conditions is between 0.119 and0.260 nMoles per gram of plasma or serum. A higher value is associatedwith a more pronounced diabetic state or increased risk.

The concentration of AC10:0 expected to provide utility for diagnosingpre-diabetes and other diabetes-related conditions is between 0.123 and0.315 nMoles per gram of plasma or serum. A higher value is associatedwith a more pronounced diabetic state or increased risk.

The concentration of PE20:4n6 expected to provide utility for diagnosingprediabetes and other diabetes-related conditions is between 21.30 and24.15 mole percent of total phosphatidylethanolamine fatty acidcomposition in plasma or serum. A higher value is associated with a morepronounced diabetic state or increased risk.

The concentration of PC18:0 expected to provide utility for diagnosingpre-diabetes and other diabetes-related conditions is between 12.40 and14.20 mole percent of total phosphatidylcholine fatty acid compositionin plasma or serum. A higher value is associated with a more pronounceddiabetic state or increased risk.

The concentration of TG14:0 expected to provide utility for diagnosingpre-diabetes and other diabetes-related conditions is between 0.07 and0.04 mole percent of total triglyceride fatty acid composition in plasmaor serum. A higher value is associated with a more pronounced diabeticstate or increased risk.

Methods of Diagnosing and Monitoring

The methods of the invention may be used to diagnose a particularcondition, for example diabetes, type 2 diabetes, insulin resistance,impaired glucose tolerance, impaired fasting glucose, prediabetes,metabolic syndrome, hepatic steatosis, insulin sensitivity,hyperinsulinemia, hepatic steatosis, muscle steatosis, hyperlipidemia,and hypercholesterolemia. The methods may also be used to assess theseverity of a diabetic condition, monitor a diabetic condition, assessthe progression or regression of a diabetic condition, and/or monitorthe response to a therapy.

For example, a method of diagnosis may comprise determining a relativeamount of one or more fatty acids to total fatty acid content in thelipids of one or more lipid classes in a sample from a body fluid of thesubject, and correlating that amount with the presence of a diabeticcondition. In some embodiments, the method may further comprise the stepof comparing the relative amount to a reference, wherein if the relativeamount is greater than the reference, diabetes, type 2 diabetes, insulinresistance, impaired glucose tolerance, impaired fasting glucose,prediabetes, metabolic syndrome, hepatic steatosis, insulin sensitivity,hyperinsulinemia, hepatic steatosis, muscle steatosis, hyperlipidemia,hypercholesterolemia, is indicated. In some embodiments, the method mayfurther comprise the step of comparing the relative amount to areference, wherein if the relative amount is less than the reference,diabetes, type 2 diabetes, insulin resistance, impaired glucosetolerance, impaired fasting glucose, prediabetes, metabolic syndrome,hepatic steatosis, insulin sensitivity, hyperinsulinemia, hepaticsteatosis, muscle steatosis, hyperlipidemia, hypercholesterolemia, isindicated.

Similarly, the severity of the diabetic condition may be measured,wherein the relative amount indicates the severity of the diabeticcondition. Additionally, the relative amount indicates the current stateof the condition, and thus a diabetic condition may be monitored and/orthe progression or regression of the condition assessed. The relativeamount may be measured at two or more time points. In some embodiments,the relative amount may be measured at 2, 3, 4, 5, 6, 7, 8, 10, 12, 15,20, or more time points. Each time point may be separated by one or morehours, days, weeks, or months. By measuring the relative amount at morethan one time point, the clinician may assess a subject's response totreatment.

Methods of Measurement of Lipid Metabolites and Biomarkers

Assays for lipid metabolite content may be performed on a body fluid ortissue sample. In one embodiment, the assays may be performed on wholeblood, plasma, serum, or isolated lipoprotein fractions. Assays for theadditional biomarkers may be performed on a body fluid or a cellularsample. These lipid metabolites and other biomarkers may readily beisolated and/or quantified by methods known to those of skill in theart, including, but not limited to, methods utilizing: mass spectrometry(MS), high performance liquid chromatography (HPLC), isocratic HPLC,gradient HPLC, normal-phase chromatography, reverse-phase HPLC, sizeexclusion chromatography, ion exchange chromatography, capillaryelectrophoresis, microfluidics, chromatography, gas chromatography (GC),thin-layer chromatography (TLC), immobilized metal ion affinitychromatography (IMAC), affinity chromatography, immunoassays, and/orcolorimetric assays. In one embodiment, the methods of the inventionutilize MS to determine lipid metabolite content. In one embodiment, themethods of the invention utilize an immunoassay to determine lipidmetabolite content. In one embodiment, the methods of the inventionutilize MS to determine the concentration of a biomarker. In oneembodiment, the methods of the invention utilize an immunoassay todetermine the concentration of a biomarker.

Various analytical methods are well known to those of skill in the art,and are further described in the following documents, which are hereinincorporated by reference in their entirety:

-   Mass Spectrometry: Cyr et al., J Chromatogr B Analyt Technol Biomed    Life Sci. 2006 Feb. 17; 832(I):24-9; Vogeser et al., Clin Chem Lab    Med. 2003 February; 41(2): 117-26.-   HPLC: Khalil et al., J Chromatogr B Analyt Technol Biomed Life Sci.    2006 May 23; Fouassier et al., J Thromb Haemost. 2006 May; 4(5):    1136-9; Badiou et al., Clin Lab. 2004; 50(3-4): 153-8; Brunelli et    al., Clin Lab. 2001; 47(7-8):393-7.-   Capillary electrophoresis: Zinellu et al., J Sep Sci. 2006 March;    29(5):704-8; Jabeen et al., Electrophoresis. 2006 May 23; Gao et    al., Electrophoresis. 2006 May; 27(9): 1784-9.-   Microfluidics: Johannessen et al., IEEE Trans Nanobioscience. 2002    March; I(I):29-36; Herrmann et al., Lab Chip. 2006 April;    6(4):555-60; Yang et al., ASAIO J. 2005 September-October;    51(5):585-90; Dupuy et al., Clin Chem Lab Med. 2005;    43(12):1291-302.-   Chromatography: Paterson et al., Addiction. 2005 December;    100(12):1832-9; Bottcher et al., J Anal Toxicol. 2005    November-December; 29(8):769-76; Julak, Prague Med Rep. 2005;    106(2):175-94; Boettcher et al., Clin Lab. 2000; 46(I-2):49-52.-   Immunoassays: Westermann et al., Clin Lab. 2002; 48(1-2):61-71;    Aoyagi et al., Clin Lab. 2001; 47(3-4): 119-27; Hubl et al., Clin    Lab. 2005; 51(11-12):641-5; Haller et al., J Anal Toxicol. 2006    March; 30(2): 106-11; Bayer et al., Clin Lab. 2005;    51(9-10):495-504; Groche et al., Clin Lab. 2003; 49(11-12):657-61;    Ivan et al., Clin Lab. 2005; 51(7-8):381-7.-   Colormetric assays: Kramer et al., Clin Chem. 2005 November;    51(11):2110-6; Groche et al., Clin Lab. 2003; 49(11-12):657-61;    Wolf, Clin Chim Acta. 2006 Mar. 24.

The TrueMass® analytical platform may also be used for the methods ofthe invention. TrueMass® is an analytical platform that may be used toget quantitative data from serum or plasma on approximately 400individual metabolites involved in structural and energetic lipidmetabolism such as triglyceride, cholesterol ester and phospholipidmetabolism. This platform is useful in profiling diseases as structuraland energetic lipids are central components of metabolism and integratedinto virtually every biological process in the body. A data set for aplasma or serum sample comprises the quantitative measurement of freecholesterol and the following fatty acids from phosphatidylcholines,phosphatidylethanolamines, lysophosphatidylcholines, triglycerides,diglycerides, free fatty acids, and cholesterol esters: 14:0, 15:0,16:0, 18:0, 20:0, 22:0, 24:0, 14:1n5, 16:1n7, tl6:1n7, 18:1n9, tl8:1n9,18:1n7, 18:2n6, tl8:2n6, 18:3n6, 18:3n3, 18:4n3, 20:1n9, 20:2n6, 20:3n9,20:3n6, 20:4n6, 20:3n3, 20:4n3, 20:5n3, 22:1n9, 22:2n6, 22:4n6, 22:5n3,22:6n3, 24:1n9, 24:6n3 and plasmalogen derivatives of 16:0, 18:0, 18:1n9and 18:1n7. Methods for using TrueMass® are known to those of skill inthe art, and are also described in the following documents, which areherein incorporated by reference in their entirety: U.S. patentapplication Ser. No. 11/296,829 (filed Dec. 6, 2005; U.S. PatentPublication No. 2006/0084129); Mutch et al., FASEB J. 2005 April;19(6):599-601; Stone et al., J Biol Chem. 2004 Mar. 19;279(12):11767-76; Watkins et al., J Nutr. 2003 November;133(11):3386-91; Watkins et al., Lipid Res. 2002 November; 43(II):1809-17.

Use of Metabolite Markers in Combination with Other Indicators/Tests

The invention further provides methods of assessing a diabetic conditionthat optionally comprise evaluating one or more risk indicators,measuring glucose levels, and/or performing another diagnostic test fora diabetic condition, in addition to measuring the level of one or moremetabolite markers described herein. A variety of risk indicators fordiabetes are known to those skilled in the art and can include, but arenot limited to, the following: age, weight, body mass index (BMI),family history (e.g., relatives with diabetes), medical history (e.g.,history of gestational diabetes), ethnic background, high bloodpressure, cholesterol levels, and activity level. In some embodiments,glucose levels are measured by fasting plasma glucose (FPG). In somealternative embodiments, glucose levels are measured by oral glucosetolerance test (OGTT). In some embodiments, one or more of themetabolite markers used herein are used in combination with a test forglycosylated hemogoblin in the blood (e.g., HbA1c), to assess a diabeticcondition.

In some embodiments, the methods, in addition to comprising measuringone or more metabolite markers such as lipid metabolites, furthercomprise (1) determining the presence or absence of one or more riskfactors for the diabetic condition, and correlating the presence orabsence of the risk factor with the presence, risk of developing, orseverity of the diabetic condition; and/or (2) measuring the level of anadditional biomarker, and correlating the level of the additionalbiomarker with the presence, risk of developing, or severity of thediabetic condition. In some embodiments, the one or more risk factorsare selected from the group consisting of: age, weight, body mass index(BMI), family history, medical history, ethnic background, high bloodpressure, cholesterol level, and activity level. In some embodiments,the additional biomarker is selected from the group consisting of bloodglucose or glycosylated hemoglobin.

Performance and Accuracy Measures of the Invention

The performance and thus absolute and relative clinical usefulness ofthe invention may be assessed in multiple ways as noted above. Among thevarious assessments of performance, the invention is intended to provideaccuracy in clinical diagnosis and prognosis. The accuracy of adiagnostic or prognostic test, assay, or method concerns the ability ofthe test, assay, or method to distinguish between subjects havingDiabetes, pre-Diabetes, or a pre-diabetic condition, or at risk forDiabetes, pre-Diabetes, or a pre-diabetic condition, is based on whetherthe subjects have an “effective amount” or a “significant alteration” inthe levels of a biomarker. By “effective amount” or “significantalteration,” it is meant that the measurement of the biomarker isdifferent than the predetermined cutoff point (or threshold value) forthat biomarker and therefore indicates that the subject has Diabetes,pre-Diabetes, or a pre-diabetic condition for which the biomarker is adeterminant. The difference in the level of biomarker between normal andabnormal is preferably statistically significant and may be an increasein biomarker level or a decrease in biomarker level. As noted below, andwithout any limitation of the invention, achieving statisticalsignificance, and thus the preferred analytical and clinical accuracy,generally but not always requires that combinations of severalbiomarkers be used together in panels and combined with mathematicalalgorithms in order to achieve a statistically significant biomarkerindex.

In the categorical diagnosis of a disease state, changing the cut pointor threshold value of a test (or assay) usually changes the sensitivityand specificity, but in a qualitatively inverse relationship. Therefore,in assessing the accuracy and usefulness of a proposed medical test,assay, or method for assessing a subject's condition, one should alwaystake both sensitivity and specificity into account and be mindful ofwhat the cut point is at which the sensitivity and specificity are beingreported because sensitivity and specificity may vary significantly overthe range of cut points. Use of statistics such as AUC, encompassing allpotential cut point values, is preferred for most categorical riskmeasures using the invention, while for continuous risk measures,statistics of goodness-of-fit and calibration to observed results orother gold standards, are preferred.

Using such statistics, an “acceptable degree of diagnostic accuracy,” isherein defined as a test or assay (such as the test of the invention fordetermining the clinically significant presence of biomarkers, whichthereby indicates the presence of Diabetes, pre-Diabetes, or apre-diabetic condition) in which the AUC (area under the ROC curve forthe test or assay) is at least 0.60, desirably at least 0.65, moredesirably at least 0.70, preferably at least 0.75, more preferably atleast 0.80, and most preferably at least 0.85.

By a “very high degree of diagnostic accuracy,” it is meant that a testor assay has an AUC (area under the ROC curve for the test or assay) ofat least 0.80, desirably at least 0.85, more desirably at least 0.875,preferably at least 0.90, more preferably at least 0.925, and mostpreferably at least 0.95.

The predictive value of any test depends both on the sensitivity andspecificity of the test, and on the prevalence of the condition in thepopulation being tested. This notion, based on Bayes' theorem, providesthat the greater the likelihood that the condition being screened for ispresent in a subject or in the population (pre-test probability), thegreater the validity of a positive test and the greater the likelihoodthat the result is a true positive. Thus, the problem with using anytest in any population where there is a low likelihood of the conditionbeing present is that a positive result has more limited value (i.e., apositive test is more likely to be a false positive). Similarly, inpopulations at very high risk, a negative test result is more likely tobe a false negative.

As a result, ROC and AUC can be misleading as to the clinical utility ofa test in low disease prevalence tested populations (defined as thosewith less than 1% rate of occurrences (incidence) per annum, or lessthan 10% cumulative prevalence over a specified time horizon).Alternatively, absolute risk and relative risk ratios as definedelsewhere in this disclosure can be employed to determine the degree ofclinical utility. Populations of subjects to be tested can also becategorized into quartiles by the test's measurement values, where thetop quartile (25% of the population) comprises the group of subjectswith the highest relative risk for developing Diabetes, pre-Diabetes, ora pre-diabetic condition and the bottom quartile comprises the group ofsubjects having the lowest relative risk for developing Diabetes,pre-Diabetes, or a pre-diabetic condition. Generally, values derivedfrom tests or assays having over 2.5 times the relative risk from top tobottom quartile in a low prevalence population are considered to have a“high degree of diagnostic accuracy,” and those with five to seven timesthe relative risk for each quartile are considered to have a “very highdegree of diagnostic accuracy.” Nonetheless, values derived from testsor assays having only 1.2 to 2.5 times the relative risk for eachquartile remain clinically useful are widely used as risk factors for adisease; such is the case with total cholesterol and for manyinflammatory biomarkers with respect to their prediction of futurecardiovascular events. Often such lower diagnostic accuracy tests mustbe combined with additional parameters in order to derive meaningfulclinical thresholds for therapeutic intervention, as is done with theaforementioned global risk assessment indices.

A health economic utility function is an yet another means of measuringthe performance and clinical value of a given test, consisting ofweighting the potential categorical test outcomes based on actualmeasures of clinical and economic value for each. Health economicperformance is closely related to accuracy, as a health economic utilityfunction specifically assigns an economic value for the benefits ofcorrect classification and the costs of misclassification of testedsubjects. As a performance measure, it is not unusual to require a testto achieve a level of performance which results in an increase in healtheconomic value per test (prior to testing costs) in excess of the targetprice of the test.

In general, alternative methods of determining diagnostic accuracy arecommonly used for continuous measures, when a disease category or riskcategory (such as pre-Diabetes) has not yet been clearly defined by therelevant medical societies and practice of medicine, where thresholdsfor therapeutic use are not yet established, or where there is noexisting gold standard for diagnosis of the pre-disease. For continuousmeasures of risk, measures of diagnostic accuracy for a calculated indexare typically based on curve fit and calibration between the predictedcontinuous value and the actual observed values (or a historical indexcalculated value) and utilize measures such as R squared,Hosmer-Lemeshow p-value statistics and confidence intervals. It is notunusual for predicted values using such algorithms to be reportedincluding a confidence interval (usually 90% or 95% CI) based on ahistorical observed cohort's predictions, as in the test for risk offuture breast cancer recurrence commercialized by Genomic Health, Inc.(Redwood City, Calif.).

In general, by defining the degree of diagnostic accuracy, i.e., cutpoints on an ROC curve, defining an acceptable AUC value, anddetermining the acceptable ranges in relative concentration of whatconstitutes an effective amount of the biomarkers of the inventionallows one of skill in the art to use the biomarkers to diagnose oridentify subjects with a predetermined level of predictability andperformance.

Calculation of the Diabetes Risk Score (DRS)

After selection of a set of biomarkers (and other optional factors) asdisclosed in the instant invention, well-known techniques such ascross-correlation, Principal Components Analysis (PCA), factor rotation,Logistic Regression (LogReg), Linear Discriminant Analysis (LDA),Eigengene Linear Discriminant Analysis (ELDA), Support Vector Machines(SVM), Random Forest (RF), Recursive Partitioning Tree (RPART), relateddecision tree classification techniques, Shrunken Centroids (SC),StepAIC, Kth-Nearest Neighbor, Boosting, Decision Trees, NeuralNetworks, Bayesian Networks, Support Vector Machines, and Hidden MarkovModels, Linear Regression or classification algorithms, NonlinearRegression or classification algorithms, analysis of variants (ANOVA),hierarchical analysis or clustering algorithms; hierarchical algorithmsusing decision trees; kernel-based machine algorithms such as kernelpartial least squares algorithms, kernel matching pursuit algorithms,kernel Fisher's discriminate analysis algorithms, or kernel principalcomponents analysis algorithms, or other mathematical and statisticalmethods can be used to develop a DRS Formula for calculation of Diabetesrisk score. A selected population of individuals is used, wherehistorical information is available regarding the values of biomarkersin the population and their clinical outcomes. To calculate a Diabetesrisk score for a given individual, biomarker values are obtained fromone or more samples collected from the individual and used as input data(inputs into a DRS Formula fitted to the actual historical data obtainedfrom the selected population of individuals).

Implementation of Biomarker Tests

Tests to measure biomarkers and biomarker panels can be implemented on awide variety of diagnostic test systems. Diagnostic test systems areapparatuses that typically include means for obtaining test results frombiological samples. Examples of such means include modules that automatethe testing (e.g., biochemical, immunological, nucleic acid detectionassays). Some diagnostic test systems are designed to handle multiplebiological samples and can be programmed to run the same or differenttests on each sample. Diagnostic test systems typically include meansfor collecting, storing and/or tracking test results for each sample,usually in a data structure or database. Examples include well-knownphysical and electronic data storage devices (e.g., hard drives, flashmemory, magnetic tape, paper print-outs). It is also typical fordiagnostic test systems to include means for reporting test results.Examples of reporting means include visible display, a link to a datastructure or database, or a printer. The reporting means can be nothingmore than a data link to send test results to an external device, suchas a data structure, database, visual display, or printer.

One embodiment of the present invention comprises a diagnostic testsystem that has been adapted to aid in the identification of individualsat risk of developing Diabetes. The test system employs means to apply aDRS Formula to inputs that include the levels of biomarkers measuredfrom a biomarker panel in accordance with the description herein.Typically, test results from a biomarker panel of the present inventionserve as inputs to a computer or microprocessor programmed with the DRSFormula. When the inputs include all the inputs for a Diabetes riskscore, then the diagnostic test system can include the score in thereported test results. If some factors apart from the biomarkers testedin the system are used to calculate the final risk score, then thesefactors can be supplied to the diagnostic test system so that it cancomplete the risk score calculation, or the DRS Formula can produce anindex score that will reported and externally combined with the otherinputs to calculate a final risk score.

A number of diagnostic test systems are available for use inimplementing the present invention and exemplify further means forcarrying out the invention. One such device is the Abbott Architect®System, a high throughput, fully automated, clinical chemistry analyzer(ARCHITECT is a registered trademark of Abbott Laboratories, AbbottPark, Ill., United States of America, for data management and laboratoryautomation systems comprised of computer hardware and software for usein the field of medical diagnostics). The Architect® system is describedat URLWorld-Wide-Web.abbottdiagnostics.com/pubs/2006/2006_AACC_Wilson_cl6000.pdf(Wilson, C. et al., “Clinical Chemistry Analyzer Sub-System LevelPerformance,” American Association for Clinical Chemistry AnnualMeeting, Chicago, Ill., Jul. 23-27, 2006, and in Kisner H J, “Productdevelopment: the making of the Abbott ARCHITECT,” Clin Lab Manage Rev.1997 November-December; 11(6):419-21; Ognibene A et al., “A new modularchemiluminescence immunoassay analyser evaluated,” Clin Chem Lab Med.2000 March; 38(3):251-60; Park J W et al., “Three-year experience inusing total laboratory automation system,” Southeast Asian J Trop MedPublic Health. 2002; 33 Suppl 2:68-73; Pauli D et al., “The AbbottArchitect c8000: analytical performance and productivity characteristicsof a new analyzer applied to general chemistry testing,” Clin Lab. 2005;51(1-2):31-41. Another useful system is the Abbott AxSYM® and AxSYM®Plus systems, which is described, along with other Abbott systems, atURLWorld-Wide-Web.abbottdiagnostics.com/Products/Instruments_by_Platform/.

Other devices useful for implementation of the tests to measurebiomarkers are the Johnson & Johnson Vitros® system (VITROS is aregistered trademark of Johnson & Johnson Corp., New Brunswick, N.J.,United States of America, for medical equipment, namely, chemistryanalyzer apparatus used to generate diagnostic test results from bloodand other body fluids by professionals in hospitals, laboratories,clinics and doctor's offices), see URLWorld-Wide-Web.jnjgateway.com/home.jhtml?loc=USENG&page=menu&nodekey=/Prod_Info/Specialty/Diagnostics/Laboratory_and_Transfusion_Medicine/Chemistry_Immunodiagnostics;and the Dade-Behring Dimension® system (DIMENSION is a registeredtrademark of Dade Behring Inc., Deerfield Ill., United States of Americafor medical diagnostic analyzers for the analysis of bodily fluids, andcomputer hardware and computer software for use in operating theanalyzers and for use in analyzing the data generated by the analyzers),see URLdiagnostics.siemens.com/webapp/wcs/stores/servlet/PSGenericDisplay˜q_catalogId˜e_-111˜a_langId˜e_-111˜a_pageId˜e_94489˜a_storeId˜e_10001.htm.

The tests for the biomarker panels of the invention can be carried outby laboratories such as those which are certified under the ClinicalLaboratory Improvement Amendments of the United States (42 U.S.C.§263(a)), or other federal, national, state, provincial, or other law ofany country, state, or province governing the operation of laboratorieswhich analyze samples for clinical purposes. Such laboratories include,for example, Laboratory Corporation of America, with headquarters at 358South Main Street, Burlington, N.C. 27215, United States of America;Quest Diagnostics, with corporate headquarters at 3 Giralda Farms,Madison, N.J. 07940, United States of America; and hospital-basedreference laboratories and clinical chemistry laboratories.

Selection of Biomarkers

Selection of lipid biomarkers is described in some detail above. Thissection emphasizes protein and other biomarkers and some other factorsthat can be used in combination with the lipid metabolites to provideenhanced predictive value.

The biomarkers and methods of the present invention allow one of skillin the art to identify, diagnose, or otherwise assess those subjects whodo not exhibit any symptoms of Diabetes, pre-Diabetes, or a pre-diabeticcondition, but who nonetheless may be at risk for developing Diabetes,pre-Diabetes, or experiencing symptoms characteristic of a pre-diabeticcondition.

Two hundred and sixty-six (266) analyte-based biomarkers have beenidentified as being found to have altered or modified presence orconcentration levels in subjects who have Diabetes, or who exhibitsymptoms characteristic of a pre-diabetic condition, or havepre-Diabetes (as defined herein), including such subjects as are insulinresistant, have altered beta cell function or are at risk of developingDiabetes based upon known clinical parameters or traditional laboratoryrisk factors, such as family history of Diabetes, low activity level,poor diet, excess body weight (especially around the waist), age greaterthan 45 years, high blood pressure, high levels of triglycerides, HDLcholesterol of less than 35, previously identified impaired glucosetolerance, previous Diabetes during pregnancy (Gestational DiabetesMellitus or GDM) or giving birth to a baby weighing more than ninepounds, and ethnicity.

Biomarkers can be selected from various groups as outlined in theinstant specification to form a panel of n markers. For example, oneembodiment of the invention embraces a method of evaluating the risk ofdeveloping Diabetes or another Diabetes-related condition, comprisingmeasuring the levels of at least three biomarkers, where two biomarkersare selected from ADIPOQ; CRP; GLUCOSE; GPT; HBA1C; HSPA1B; IGFBP1;IGFBP2; INS; LEP; and TRIG; and one biomarker is selected from theALLDBRISKS, CPs, and TLRFs of Table 7, Table 8, and Table 9 and usingthe measured levels of the biomarkers to evaluate the risk of developingDiabetes or a Diabetes-related condition. In this instance, n is 3. Whenselecting from different groups, unique biomarkers should be used; e.g.,in the immediately preceding example, if ADIPOQ is selected from thegroup of ADIPOQ; CRP; GLUCOSE; GPT; HBA1C; HSPA1B; IGFBP1; IGFBP2; INS;LEP; and TRIG, then ADIPOQ should not also be selected from the markersof Table 7, Table 8, and Table 9. Diabetes-related conditions includeDiabetes and the pre-diabetic conditions defined above.

Table 7 comprises several biomarkers, collectively referred to asALLDBRISK, which are analyte-based or individual history-basedbiomarkers for use in the present invention. One skilled in the art willrecognize that the ALLDBRISKS presented herein encompasses all forms andvariants, including but not limited to, polymorphisms, isoforms,mutants, derivatives, precursors including nucleic acids andpro-proteins, cleavage products, receptors (including soluble andtransmembrane receptors), ligands, protein-ligand complexes, andpost-translationally modified variants (such as cross-linking orglycosylation), fragments, and degradation products, as well as anymulti-unit nucleic acid, protein, and glycoprotein structures comprisedof any of the ALLDBRISKS as constituent subunits of the fully assembledstructure.

TABLE 7 Entrez Gene ALLDBRISK Official Name Common Name Link 1ATP-binding cassette, sub- sulfonylurea receptor (SUR1), H1; ABCC8family C (CFTR/MRP), member 8 SUR; HHF1; MRP8; PHHI; SUR1; ABC36; HRINS2 ATP-binding cassette, sub- sulfonylurea receptor (SUR2a), SUR2; ABCC9family C (CFTR/MRP), member 9 ABC37; CMD1O; FLI36852 3 angiotensinI-converting angiotensin-converting enzyme (ACE) - ACE enzyme(peptidyl-dipeptidase ACE1, CD143, DCP, DCP1, CD143 A) 1 antigen;angiotensin I-converting enzyme; angiotensin-converting enzyme, somaticisoform; carboxycathepsin; dipeptidyl carboxypeptidase 1; kininase II;peptidase P; peptidyl-dipeptidase A; testicular ECA 4 adenylatecyclase-activating adenylate cyclase-activating ADCYAP1 polypeptide 1(pituitary) polypeptide 5 adiponectin, C1Q and collagen Adiponectin -ACDC, ACRP30, APM-1, ADIPOQ domain containing APM1, GBP28, glycosylatedadiponectin, adiponectin, adipocyte, C1Q and collagen domain containing;adipocyte, C1Q and collagen domain- containing; adiponectin; adiposemost abundant gene transcript 1; gelatin- binding protein 28 6adiponectin receptor 1 G proteincoupled receptor AdipoR1 - ADIPOR1ACDCR1, CGI-45, PAQR1, TESBP1A 7 adiponectin receptor 2 G proteincoupledreceptor AdipoR2 - ADIPOR2 ACDCR2, PAQR2 8 Adrenomedullinadrenomedullin - AM, ADM preproadrenomedullin 9 adrenergic beta-2receptor, G protein-coupled beta-2 adrenoceptor - ADRB2 surface ADRB2R,ADRBR, B2AR, BAR, BETA2AR, beta-2 adrenergic receptor; beta-2adrenoceptor; catecholamine receptor 10 advanced glycosylation endRAGE - advanced glycosylation end AGER product-specific receptorproduct-specific receptor RAGE3; advanced glycosylation end product-specific receptor variant sRAGE1; advanced glycosylation end product-specific receptor variant sRAGE2; receptor for advanced glycosylationend products; soluble receptor 11 agouti-related protein homolog AGRT,ART, ASIP2, & Agouti-related AGRP (mouse) transcript, mouse, homolog of;agouti (mouse)-related protein; agouti-related protein homolog 12angiotensinogen (serpin angiotensin I; pre-angiotensinogen; AGTpeptidase inhibitor, clade A, angiotensin II precursor; member 8)angiotensinogen (serine (or cysteine) peptidase inhibitor, clade A,member 8); angiotensinogen (serine (or cysteine) proteinase inhibitor,clade A (alpha-1 antiproteinase, antitrypsin), member 8) 13 angiotensinII receptor, type 1 G protein-coupled receptor AGTR1A - AGTR1 AG2S,AGTR1A, AGTR1B, AT1, AT1B, AT2R1, AT2R1A, AT2R1B, HAT1R, angiotensinreceptor 1; angiotensin receptor 1B; type-1B angiotensin II receptor 14angiotensin II receptor- angiotensin II - ATRAP, ATI receptor- AGTRAPassociated protein associated protein; angiotensin II, type Ireceptor-associated protein 15 alpha-2-IIS-glycoprotein A2HS, AHS,FETUA, HSGA, Alpha- AHSG 2HS-glycoprotein; fetuin-A 16 v-akt murinethymoma viral Ser/Thr kinase Akt - PKB, PRKBA, AKT1 oncogene homolog 1RAC, RAC-ALPHA, RAC-alpha serine/threonine-protein kinase; murinethymoma viral (v-akt) oncogene homolog-1; protein kinase B; rac proteinkinase alpha 17 v-akt murine thymoma viral PKBBETA, PRKBB, RAC-BETA,AKT2 oncogene homolog 2 Murine thymoma viral (v-akt) homolog- 2; racprotein kinase beta 18 Albumin Ischemia-modified albumin (IMA) - cellALB growth-inhibiting protein 42; growth- inhibiting protein 20; serumalbumin 19 Alstrom syndrome 1 ALSS ALMS1 20 archidonate 12-lipoxygenaseLOG12, 12(S)-lipoxygenase; platelet- ALOX12 type12-lipoxygenase/arachidonate 12- lipoxygenase 21 Angiogenin,ribonuclease, Angiogenin, MGC71966, RNASE4, ANG RNase A family, 5RNASE5, angiogenin, ribonuclease, RNase A family, 5 22 ankyrin repeatdomain 23 DARP, MARP3, Diabetes-related ANKRD23 ankyrin repeat protein;muscle ankyrin repeat protein 3 23 apelin, AGTRL 1 Ligand XNPEP2,apelin, peptide ligand for APLN APJ receptor 24 apolipoprotein A-Iapolipoproteins A-1 and B, APOA1 amyloidosis; apolipoprotein A-I,preproprotein; apolipoprotein A1; preproapolipoprotein 25 apolipoproteinA-II Apolipoprotein A-II APOA2 26 apolipoprotein B (includingapolipoproteins A-1 and B - APOB Ag(x) antigen) Apolipoprotein B, FLDB,apoB-100; apoB-48; apolipoprotein B; apolipoprotein B48 27apolipoprotein E APO E - AD2, apoprotein, Alzheimer APOE disease 2(APOE*E4-associated, late onset); apolipoprotein E precursor;apolipoprotein E3 28 aryl hydrocarbon receptor dioxin receptor, nucleartranslocator; ARNT nuclear translocator hypoxia-inducible factor 1, betasubunit 29 aryl hydrocarbon receptor Bmal1, TIC; JAP3; MOP3; BMAL1;ARNTL nuclear translocator-like PASD3; BMAL1c; bHLH-PAS protein JAP3;member of PAS superfamily 3; ARNT-like protein 1, brain and muscle;basic-helix-loop-helix-PAS orphan MOP3 30 arrestin, beta 1 betaarrestin - ARB1, ARR1, arrestin ARRB1 beta 1 31 arginine vasopressincopeptin - ADH, ARVP, AVP-NPII, AVP (neurophysin II, antidiuretic AVRP,VP, arginine vasopressin- hormone, Diabetes insipidus, neurophysin II;vasopressin- neurohypophyseal) neurophysin II-copeptin, vasopressin 32bombesin receptor subtype 3 G protein-coupled receptor; bombesin BRS3receptor subtype 3 33 Betacellulin Betacellulin BTC 34 benzodiazepinereceptor PBR - DBI, IBP, MBR, PBR, PKBS, BZRP (peripheral) PTBR, mDRC,pk18, benzodiazepine peripheral binding site; mitochondrialbenzodiazepine receptor; peripheral benzodiazepine receptor; peripheralbenzodiazepine receptor; peripheral- type benzodiazepine receptor 35complement component 3 complement C3 - acylation-stimulating C3 proteincleavage product; complement component C3, ASP; CPAMD1 36 complementcomponent 4A complement C4 - C4A anaphylatoxin; C4A (Rodgers bloodgroup) Rodgers form of C4; acidic C4: c4 propeptide; complementcomponent 4A; complement component C4B 37 complement component 4B C4A,C4A13, C4A91, C4B1, C4B12, C4B (Childo blood group) C4B2, C4B3, C4B5,C4F, CH, CO4, CPAMD3, C4 complement C4d region; Chido form of C4; basicC4; complement C4B; complement component 4B; complement component 4B,centromeric; complement component 4B, telomeric; complement componentC4B 38 complement component 5 anaphylatoxin C5a analog - CPAMD4 C5 39Calpain-10 calcium-activated neutral protease CAPN10 40 Cholecystokinincholecystokinin CCK 41 cholecystokinin (CCK)-A CCK-A; CCK-A; CCKRA;CCK1-R; CCKAR receptor cholecystokinin-1 receptor; cholecystokinintype-A receptor 42 chemokine (C-C motif) ligand 2 Monocytechemoattractant protein-1 CCL2 (MCP-1) - GDCF-2, GDCF-2 HC11, HC11,HSMCR30, MCAF, MCP-1, MCP1, SCYA2, SMC-CF, monocyte chemoattractantprotein-1; monocyte chemotactic and activating factor; conocytechemotactic protein 1, homologous to mouse Sig-je; monocyte secretoryprotein JE; small inducible cytokine A2; small inducible cytokine A2(monocyte chemotactic protein 1, homologous to mouse Sig- je); smallinducible cytokine subfamily A (Cys-Cys), member 2 43 CD14 molecule CD14antigen - monocyte receptor CD14 44 CD163 molecule CD163 - M130, MM130 -CD163 CD163 antigen; macrophage-associated antigen, macrophage-specificantigen 45 CD36 molecule fatty acid translocase, FAT; GP4; CD36(thrombospondin receptor) GP3B; GPIV; PASIV; SCARB3, PAS-4 protein;collagen type I; glycoprotein IIIb; cluster determinant 36; fatty acidtranslocase; thrombospondin receptor; collagen type I receptor; plateletglycoprotein IV; platelet collagen receptor; scavenger receptor class B,member 3; leukocyte differentiation antigen CD36; CD36 antigen (collagentype I receptor, thrombospondin receptor) 46 CD38 molecule T10; CD38antigen (p45); cyclic ADP- CD38 ribose hydrolase; ADP-ribosylcyclase/cyclic ADP-ribose hydrolase 47 CD3d molecule, delta (CD3-CD3-DELTA, T3D, CD3D antigen, CD3D TCR complex) delta polypeptide; CD3dantigen, delta polypeptide (TiT3 complex); T-cell receptor T3 deltachain 48 CD3g molecule, gamma (CD3- T3G; CD3-GAMMA, T3G, CD3G CD3G TCRcomplex) gamma; CD3g antigen, gamma polypeptide (TiT3 complex); T-cellantigen receptor complex, gamma subunit of T3; T-cell receptor T3 gammachain; T-cell surface glycoprotein CD3 gamma chain precursor 49 CD40molecule, TNF receptor Bp50, CDW40, TNFRSF5, p50, B cell CD40superfamily member 5 surface antigen CD40; B cell- associated molecule;CD40 antigen; CD40 antigen (TNF receptor superfamily member 5); CD40type II isoform; CD40L receptor; nerve growth factor receptor-relatedB-lymphocyte activation molecule; tumor necrosis factor receptorsuperfamily, member 5 50 CD40 ligand (TNT superfamily, CD40 Ligand(CD40L) (also called CD40LG member 5, hyper-IgM soluble CD40L vs.platelet-bound syndrome) CD40L), CD154, CD40L, HIGM1, IGM, IMD3, T-BAM,TNFSF5, TRAP, gp39, hCD40L, CD40 antigen ligand; CD40 ligand; T-Bcell-activating molecule; TNF-related activation protein; tumor necrosisfactor (ligand) superfamily member 5; tumor necrosis factor (ligand)superfamily, member 5 (hyper- IgM syndrome); tumor necrosis factorligand superfamily member 5 51 CD68 molecule GP110; SCARD1; macrosialin;CD68 CD68 antigen; macrophage antigen CD68; scavenger receptor class D,member 1 52 cyclin-dependent kinase 5 PSSALRE; cyclin-dependent kinase 5CDK5 53 complement factor D (adipsin) ADN, DF, PFD, C3 convertase CFDactivator; D component of complement (adipsin); adipsin; complementfactor D; properdin factor D 54 CASP8 and FADD-like FLIP - caspase 8inhibitor, CASH; CFLAR apoptosis regulator FLIP; MRIT; CLARP; FLAME;Casper; c-FLIP; FLAME-1; I-FLICE; USURPIN; c-FLIPL; c-FLIPR; c-FLIPS;CASP8AR1, usurpin beta; FADD-like anti-apoptotic molecule; Inhibitor ofFLICE; Caspase-related inducer of apoptosis; Caspase homolog;Caspase-like apoptosis regulatory protein 55 Clock homolog (mouse) clockprotein; clock (mouse) homolog; CLOCK circadian locomoter output cycleskaput protein 56 chymase 1, mast cell chymase 1 - CYH, MCT1, chymase 1CMA1 preproprotein transcript E; chymase 1 preproprotein transcript I;chymase, heart; chymase, mast cell; mast cell protease I 57 cannabinoidreceptor 1 (brain) cannabinoid receptor 1 - CANN6, CB- CNR1 R, CB1,CB1A, CB1K5, CNR, central cannabinoid receptor 58 cannabinoid receptor 2cannabinoid receptor 2 (macrophage), CNR2 (macrophage) CB2, CX5 59Cortistatin CST-14; CST-17; CST-29; cortistatin- CORT 14;cortistatin-17; cortistatin-29; preprocortistatin 60 carnitinepalmitoyltransferase I CPT1; CPT1-L; L-CPT1, carnitine CPT1Apalmitoyltransferase I; liver 61 carnitine palmitoyltransferase II CPT1,CPTASE CPT2 62 complement component (3b/4b) complement receptor CR1; KN;C3BR; CR1 receptor 1 CD35; CD35 antigen; C3b/C4b receptor; C3-bindingprotein; Knops blood group antigen; complement component receptor 1;complement component (3b/4b) receptor 1, including Knops blood groupsystem 63 complement component complement receptor CR2; C3DR; CR2(3d/Epstein-Barr virus) receptor 2 CD21 64 CREB binding protein Cbp;CBP; RTS; RSTS, CREB-binding CREBBP (Rubinstein-Taybi syndrome) protein65 C-reactive protein, pentraxin- C-Reactive Protein, CRP, PTX1 CRPrelated 66 CREB regulated transcription Torc2 (transcriptionalcoactivator); CRTC2 coactivator 2 transducer of regulated cAMP responseelement-binding protein (CREB) 2 67 colony stimulating factor 1 M-CSF -colony stimulating factor 1; CSF1 (macrophage) macrophage colonystimulating factor 68 cathepsin B cathepsin B - procathepsin B, APPS;CTSB CPSB, APP secretase; amyloid precursor protein secretase; cathepsinB1; cysteine protease; preprocathepsin B 69 cathepsin L CATL, MEP, majorexcreted protein CTSL 70 cytochrome P450, family 19, ARO, ARO1, CPV1,CYAR, CYP19, P- CYP19A1 subfamily A, polypeptide 1 450AROM, aromatase;cytochrome P450, family 19; cytochrome P450, subfamily XIX(aromatization of androgens); estrogen synthetase; flavoprotein-linkedmonooxygenase; microsomal monooxygenase 71 Dio-2, deathinducer-obliterator 1 death-associated transcription factor 1; DIDO1BYE1; DIO1; DATF1; DIDO2; DIDO3; DIO-1 72 dipeptidyl-peptidase 4 (CD26,dipeptidylpeptidase IV - ADABP, DPP4 adenosine deaminase ADCP2, CD26,DPPIV, TP103, T-cell complexing protein 2) activation antigen CD26;adenosine deaminase complexing protein 2; dipeptidylpeptidase IV;dipeptidylpeptidase IV (CD26, adenosine deaminase complexing protein 2)73 epidermal growth factor (beta- URG - urogastrone EGF urogastrone) 74early growth response 1 zinc finger protein 225; transcription EGR1factor ETR103; early growth response protein 1; nerve growthfactor-induced protein A 75 epididymal sperm binding E12, HE12,epididymal secretory ELSPBP1 protein 1 protein 76 ectonucleotide ENPP1 -M6S1, NPP1, NPPS, PC-1, ENPP1 pyrophosphatase/ PCA1, PDNP1,Ly-41-antigen; alkaline phosphodiesterase 1 phosphodiesterase 1;membrane component, chromosome 6, surface marker 1; phosphodiesteraseI/nucleotide pyrophosphatase 1; plasma-cell membrane glycoprotein 1 77E1A binding protein p300 p300, E1A binding protein p300, E1A- EP300binding protein, 300 kD; E1A- associated protein p300 78 coagulationfactor XIII, A1 Coagulation Factor XIII - Coagulation F13A1 polypeptidefactor XIII A chain; Coagulation factor XIII, A polypeptide; TGase;(coagulation factor XIII, A1 polypeptide); coagulation factor XIII A1subunit; factor XIIIa, coagulation factor XIII A1 subunit 79 coagulationfactor VIII, Factor VIII, AHF, F8 protein, F8B, F8C, F8 procoagulantcomponent FVIII, HEMA, coagulation factor VIII; (hemophilia A)coagulation factor VIII, isoform b; coagulation factor VIIIc; factorVIII F8B; procoagulant component, isoform b 80 fatty acid bindingprotein 4, fatty acid binding protein 4, adipocyte - FABP4 adipocyteA-FABP 81 Fas (TNF receptor superfamily, soluble Fas/APO-1 (sFas),ALPS1A, FAS member 6) APO-1, APT1, Apo-1 Fas, CD95, FAS1, FASTM,TNFRSF6, APO-1 cell surface antigen; CD95 antigen; Fas antigen;apoptosis antigen 1; tumor necrosis factor receptor superfamily, member6 82 Fas ligand (TNF superfamily, Fas ligand (sFasL), APT1LG1, CD178,FASLG member 6) CD95L, FASL, TNFSF6, CD95 ligand; apoptosis (APO-1)antigen ligand 1; fas ligand; tumor necrosis factor (ligand)superfamily, member 6 83 free fatty acid receptor 1 G protein-coupledreceptor 40 - FFAR1 FFA1R, GPR40, G protein-coupled receptor 40 84fibrinogen alpha chain Fibrin, Fib2, fibrinogen, A alpha FGApolypeptide; fibrinogen, alpha chain, isoform alpha preproprotein;fibrinogen, alpha polypeptide 85 forkhead box A2 (Foxa2); HNF3B; TCF3B;hepatic FOXA2 nuclear factor-3-beta; hepatocyte nuclear factor 3, beta86 forkhead box O1A FKHI, FKHR; FOXO1; forkhead FOXO1A (Drosophila)homolog 1 (rhabdomyosarcoma); forkhead, Drosophila, homolog of, inrhabdomyosarcoma 87 Ferritin FTH; PLIF; FTHL6; PIG15; apoferritin; FTH1placenta immunoregulatory factor; proliferation-inducing protein 15 88glutamate decarboxylase 2 glutamic acid decarboxylase (GAD65) GAD2antibodies; Glutamate decarboxylase-2 (pancreas); glutamatedecarboxylase 2 (pancreatic islets and brain, 65 kD) 89 Galanin GALN;GLNN; galanin-related peptide GAL 90 Gastrin gastrin - GAS GAST 91glucagon glucagon-like peptide-1, GLP-1, GLP2, GCG GRPP,glicentin-related polypeptide; glucagon-like peptide 1; glucagon-likepeptide 2 92 Glucokinase hexokinase 4, maturity to onset GCK Diabetes ofthe young 2; GK; GLK; HK4, HHF3; HKIV; HXKP; MODY2 93gamma-glutamyltransferase 1 GGT; GTG; CD224; glutamyl GGT1transpeptidase; gamma-glutamyl transpeptidase 94 growth hormone 1 growthhormone - GH, GH-N, GHN, GH1 hGH-N, pituitary growth hormone 95ghrelin/obestatin ghrelin - MTLRP, ghrelin, obestatin, GHRLpreprohormone ghrelin; ghrelin precursor; ghrelin, growth hormonesecretagogue receptor ligand; motilin-related peptide 96 gastricinhibitory polypeptide glucose-dependent insulinotropic GIP peptide 97gastric inhibitory polypeptide GIP Receptor GIPR receptor 98glucagon-like peptide 1 glucagon-like peptide 1 receptor GLP1R receptor99 guanine nucleotide binding G-protein beta-3 subunit - G protein, GNB3protein (G Protein), beta beta-3 subunit; GTP-binding regulatorypolypeptide 3 protein beta-3 chain; guanine nucleotide-binding proteinG(I)/G(S)/G(T) beta subunit 3; guanine nucleotide-binding protein,beta-3 subunit; hypertension associated protein; transducin beta chain 3100 glutamic-pyruvate transaminase glutamic-pyruvate transaminase GPT(alanine aminotransferase) (alanine aminotransferase), AAT1 ALT1, GPT1101 gastrin releasing peptide bombesin; BN; GRP-10; proGRP; GRP(bombesin) preproGRP; neuromedin C; pre- progastrin releasing peptide102 gelsolin (amyloidosis, Finnish Gelsolin GSN type) 103 HemoglobinCD31; alpha-1 globin; alpha-1-globin; HBA1 alpha-2 globin;alpha-2-globin; alpha one globin; hemoglobin alpha 2; hemoglobinalpha-2; hemoglobin alpha-1 chain; hemoglobin alpha 1 globin chain,glycosylated hemoglobin, IIBA1c 104 hemoglobin, beta HBD, beta globinHBB 105 hypocretin (orexin) orexin A; OX; PPDX HCRT neuropeptideprecursor 106 hepatocyte growth factor Hepatocyte growth factor (HGF) -F- HGF (hepapoietin A; scatter factor) TCF, HGFB, HPTA, SF, fibroblast-derived tumor cytotoxic factor; hepatocyte growth factor; hepatopoietinA; lung fibroblast-derived mitogen; scatter factor 107 hepatocytenuclear factor 4, hepatocyte nuclear factor 4 - HNF4, HNF4A alphaHNF4a7, HNF4a8, HNF4a9, MODY, MODY1, NR2A1, NR2A21, TCF, TCF14,HNF4-alpha; hepatic nuclear factor 4 alpha; hepatocyte nuclear factor 4alpha; transcription factor-14 108 haptoglobin haptoglobin - hp2-alphaHP 109 hydroxysteroid (11-beta) Corticosteroid 11-beta-dehydrogenase,HSD11B1 dehydrogenase 1 isozyme 1; HDL; 11-DH; HSD11; HSD11B; HSD11L;11-beta-HSD1 110 heat shock 70 kDa protein 1B HSP70-2, heat shock 70 kDprotein 1B HSPA1B 111 islet amyloid polypeptide Amylin - DAP, IAP, Isletamyloid IAPP polypeptide (Diabetes-associated peptide; amylin) 112intercellular adhesion molecule soluble intercellular adhesion ICAM1 1(CD54), human rhinovirus molecule-1, BB2, CD54, P3.58, 60 bp receptorafter segment 1; cell surface glycoprotein; cell surface glycoproteinP3.58; intercellular adhesion molecule 1 113 intercellular adhesionmolecule CD50, CDW50, ICAM-R intercellular ICAM3 3 (CD50) adhesionmolecule-3 114 interferon, gamma IFNG: IFG; IFI IFNG 115 insulin-likegrowth factor 1 IGF-1; somatomedin C, insulin-like IGF1 (somatomedin C)growth factor-1 116 insulin-like growth factor 2 IGF-II polymorphisms(somatomedin IGF2 (somatomedin A) A) - C11orf43, INSIGF, pp9974,insulin-like growth factor 2; insulin-like growth factor II;insulin-like growth factor type 2; putative insulin-like growth factorII associated protein 117 insulin-like growth factor insulin-like growthfactor binding IGFBP1 binding protein 1 protein-1 (IGFBP-1) - AFBP,IBP1, IGF-BP25, PP12, hIGFBP-1, IGF- binding protein 1; alpha-pregnancy-associated endometrial globulin; amniotic fluid binding protein; bindingprotein-25; binding protein-26; binding protein-28; growth hormoneindependent-binding protein; placental protein 12 118 insulin-likegrowth factor insulin-like growth factor binding IGFBP3 binding protein3 protein 3: IGF-binding protein 3 - BP- 53, IBP3, IGF-binding protein3; acid stable subunit of the 140 K IGF complex; binding protein 29;binding protein 53; growth hormone-dependent binding protein 119inhibitor of kappa light ikk-beta; IKK2; IKKB; NFKBIKB; IKK- IKBKBpolypeptide gene enhancer in beta; nuclear factor NF-kappa-B B-cells,kinase beta inhibitor kinase beta; inhibitor of nuclear factor kappa Bkinase beta subunit 120 interleukin 10 IL-10, CSIF, IL-10, IL10A, TGIF,IL10 cytokine synthesis inhibitory factor 121 interleukin 18(interferon- IL-18 - 1GIF, IL-18, IL-1g, IL1F4, IL-1 IL18 gamma-inducingfactor) gamma; interferon-gamma-inducing factor; interleukin 18;interleukin-1 gamma; interleukin-18 122 interleukin 1, alpha IL 1 -IL-1A, IL1, IL1-ALPHA, IL1F1, IL1A IL1A (IL1F1); hematopoietin-1;preinterleukin 1 alpha; pro-interleukin- 1-alpha 123 interleukin 1, betainterleukin-1 beta (IL-1 beta) - IL-I, IL1- IL1B BETA, IL1F2, catabolin;preinterleukin 1 beta; pro-interleukin-1-beta 124 interleukin 1 receptorinterleukin-1 receptor antagonist. (IL- IL1RN antagonist 1Ra) -ICIL-1RA, IL-1ra3, IL1F3, IL1RA, IRAP, IL1RN (IL1F3); intracellular IL-1receptor antagonist type II; intracellular interleukin-1 receptorantagonist (icIL-1ra); type II interleukin-1 receptor antagonist 125interleukin 2 interleukin-2 (IL-2) - IL-2, TCGF, IL2 lymphokine, T cellgrowth factor; aldesleukin; interleukin-2; involved in regulation ofT-cell clonal expansion 126 interleukin 2 receptor, alpha Interleukin-2receptor; IL-2RA; IL2RA; IL2RA RP11-536K7.1; CD25; IDDM10; IL2R; TCGFR;interleukin 2 receptor, alpha chain 127 interleukin 6 (interferon, beta2) Interleukin-6 (IL-6), BSF2, HGF, HSF, IL6 IFNB2, IL-6 128 interleukin6 receptor interleukin-6 receptor, soluble (sIL-6R) - IL6R CD126,IL-6R-1, IL-6R-alpha, IL6RA, CD126 antigen; interleukin 6 receptor alphasubunit 129 interleukin 6 signal transducer CD130, CDw130, GP130, GP130-I16ST (gp130, oncostatin M receptor) RAPS, IL6R-beta; CD130 antigen;IL6ST nirs variant 3; gp130 of the rheumatoid arthritis antigenicpeptide- bearing soluble form; gp130 transducer chain; interleukin 6signal transducer; interleukin receptor beta chain; membraneglycoprotein gp130; oncostatin M receptor 130 interleukin 8Interleukin-8 (IL-8), 3-10C, AMCF-I, IL8 CXCL8, GCP-1, GCP1, IL-8, K60,LECT, LUCT, LYNAP, MDNCF, MONAP, NAF, NAP-1, NAP1, SCYB8, TSG-1, b-ENAP,CXC chemokine ligand 8; LUCT/interleukin-8; T cell chemotactic factor;beta- thromboglobulin-like protein; chemokine (C—X—C motif) ligand 8;emoctakin; granulocyte chemotactic protein 1; lymphocyte-derivedneutrophil-activating factor; monocyte derived neutrophil-activatingprotein; monocyte-derived neutrophil chemotactic factor; neutrophil-activating factor; neutrophil-activating peptide 1;neutrophil-activating protein 1; protein 3-10C; small inducible cytokinesubfamily B, member 8 131 inhibin, beta A (activin A, activin activinA - EDF, FRP, Inhibin, beta-1; INHBA AB alpha polypeptide) inhibin betaA 132 insulin Insulin (mature polypeptide) INSULIN-M 133 insulinreceptor CD220, HHF5 INSR 134 insulin promoter factor-1 IPF-1, PDX-1(pancreatic and IPF1 duodenal homeobox factor-1) 135 insulin receptorsubstrate 1 HIRS-1 IRS1 136 insulin receptor substrate-2 IRS2 IRS2 137potassium inwardly-rectifying ATP gated K⁺ channels, Kir 6.2; BIR;KCNJ11 channel, subfamily J, member HHF2; PHHI; IKATP; KIR6.2 11 138potassium inwardly-rectifying ATP gated K⁺ channels, Kir 6.1 KCNJ8channel, subfamily J, member 8 139 klotho klotho KL 140 kallikrein B,plasma (Fletcher kallikrein 3 - KLK3 - Kallikrein, plasma; KLKB1 factor)1 kallikrein 3, plasma; kallikrein B plasma; kininogenin; plasmakallikrein B1 141 leptin (obesity homolog, mouse) leptin - OB, OBS,leptin; leptin (murine LEP obesity homolog); obesity; obesity (murinehomolog, leptin) 142 leptin receptor leptin receptor, soluble - CD295,OBR, LEPR OB receptor 143 legumain putative cysteine protease 1 - AEP,LGMN LGMN1, PRSC1, asparaginyl endopeptidase; cysteine protease 1;protease, cysteine, 1 (legumain) 144 lipoprotein, Lp(a) lipoprotein (a)[Lp(a)], AK38, APOA, LPA LP, Apolipoprotein Lp(a); antiangiogenic AK38protein; apolipoprotein(a) 145 lipoprotein lipase LPL - LIPD LPL 146v-maf musculoaponeurotic MafA (transcription factor) - RIPE3b1, MAFAfibrosarcoma oncogene hMafA, v-maf musculoaponeurotic homolog A (avian)fibrosarcoma oncogene homolog A 147 mitogen-activated protein IB1,JIP-1, JIP1, PRKM8IP, JNK- MAPK8IP1 kinase 8 interacting protein 1interacting protein 1; PRKM8 interacting protein; islet-brain I 148mannose-binding lectin (protein COLEC1, HSMBPC, MBL, MBP, MBL2 C) 2,soluble (opsonic defect) MBP1, Mannose-binding lectin 2, soluble(opsonic defect); mannan- binding lectin; mannan-binding protein;mannose binding protein; mannose- binding protein C; soluble mannose-binding lectin 149 melanocortin 4 receptor G protein-coupled receptorMC4 MC4R 150 melanin-concentrating hormone G Protein-Coupled Receptor24 - MCHR1 receptor 1 GPR24, MCH1R, SLC1, G protein- coupled receptor24; G-protein coupled receptor 24 isoform 1, GPCR24 151 matrixmetallopeptidase 12 Matrix Metalloproteinases (MMP), MMP12 (macrophageelastase) HME, MME, macrophage elastase; macrophage metalloelastase;matrix metalloproteinase 12; matrix metalloproteinase 12 (macrophageelastase) 152 matrix metallopeptidase 14 Matrix Metalloproteinases(MMP), MMP14 (membrane-inserted) MMP-X1, MT1-MMP, MTMMP1, matrixmetalloproteinase 14; matrix metalloproteinase 14 (membrane- inserted);membrane type 1 metalloprotease; membrane-type matrix metalloproteinase1; membrane-type-1 matrix metalloproteinase 153 matrix metallopeptidase2 Matrix Metalloproteinases (MMP), MMP2 (gelatinase A, 72 kDa MMP-2,CLG4, CLG4A, MMP-II, gelatinase, 72 kDa type IV MONA, TBE-1, 72 kD typeIV collagenase) collagenase; collagenase type IV-A; matrixmetalloproteinase 2; matrix metalloproteinase 2 (gelatinase A, 72 kDgelatinase, 72 kD type IV collagenase); matrix metalloproteinase 2(gelatinase A, 72 kDa gelatinase, 72 kDa type IV collagenase); matrixmetalloproteinase-II; neutrophil gelatinase 154 matrix metallopeptidase9 Matrix Metalloproteinases (MMP), MMP9 (gelatinase B, 92 kDa MMP-9,CLG4B, GELB, 92 kD type IV gelatinase, 92 kDa type IV collagenase;gelatinase B; collagenase) macrophage gelatinase; matrixmetalloproteinase 9; matrix metalloproteinase 9 (gelatinase B, 92 kDgelatinase, 92 kD type IV collagenase); matrix metalloproteinase 9(gelatinase B, 92 kDa gelatinase, 92 kDa type IV collagenase); type Vcollagenase 155 nuclear receptor co-repressor 1 NCoR; thyroid hormone-and retinoic NCOR1 acid receptor-associated corepressor 1 156 neurogenicdifferentiation 1 neuroD (transcription factor) - BETA2, NEUROD1 BHF-1,NEUROD 157 nuclear factor of kappa light nuclear factor, kappa B (NFKB):DNA NFKB1 polypeptide gene enhancer in binding factor KBF1; nuclearfactor NF- B-cells 1(p105) kappa-B p50 subunit; nuclear factor kappa-BDNA binding subunit 158 nerve growth factor, beta B-type neurotrophicgrowth factor NGFB polypeptide (BNGF) - beta-nerve growth factor; nervegrowth factor, beta subunit 159 non-insulin-dependent Diabetes NIDDM1NIDDM1 Mellitus (common, type 2) 1 160 non-insulin-dependent DiabetesNIDDM2 NIDDM2 Mellitus (common, type 2) 2 161 non-insulin-dependentDiabetes NIDDM3 NIDDM3 Mellitus 3 162 nischarin (imidazoline receptor)imidazoline receptor; IRAS; I-1 NISCH receptor candidate protein;imidazoline receptor candidate; imidazoline receptor antisera selected163 NF-kappa B repressing factor NRF; ITBA4 gene; transcription factorNKRF NRF; NF-kappa B repressing factor; NF-kappa B-repressing factor 164neuronatin Peg5 NNAT 165 nitric oxide synthase 2A NOS, type II; nitricoxide synthase, NOS2A macrophage 166 Niemann-Pick disease, type C2epididymal secreting protein 1 - HE1, NPC2 NP-C2, epididymal secretoryprotein; epididymal secretory protein E1; tissue-specific secretoryprotein 167 natriuretic peptide precursor B B-type Natriurctic Peptide(BNP), BNP, NPPB brain type natriuretic peptide, pro- BNP?, NPPB 168nuclear receptor subfamily 1, Human Nuclear Receptor NR1D1 - NR1D1 groupD, member 1 EAR1, THRA1, THRAL, ear-1, hRev, Rev-erb-alpha; thyroidhormone receptor, alpha-like 169 nuclear respiratory factor 1 NRF1;ALPHA-PAL; alpha NRF1 palindromic-binding protein 170 oxytocin,prepro-(neurophysin oxytocin - OT, OT-NPI, oxytocin- OXT I) neurophysinI; oxytocin-neurophysin I, preproprotein 171 purinergic receptor P2Y, G-G protein-coupled receptor P2Y10 - P2RY10 protein coupled, 10 P2Y10,G-protein coupled purinergic receptor P2Y10; P2Y purinoceptor 10;P2Y-like receptor 172 purinergic receptor P2Y, G- G protein-coupledreceptor P2Y12 - P2RY12 protein coupled, 12 ADPG-R, HORK3, P2T(AC),P2Y(AC), P2Y(ADP), P2Y(eye), P2Y12, SP1999, ADP-glucose receptor;G-protein coupled receptor SP1999; Gi-coupled ADP receptor HORK3; P2Ypurinoceptor 12; platelet ADP receptor; purinergic receptor P2RY12;purinergic receptor P2Y, G-protein coupled 12; purinergic receptorP2Y12; putative G- protein coupled receptor 173 purinergic receptor P2Y,G- Purinoceptor 2 Type Y (P2Y2) - HP2U, P2RY2 protein coupled, 2 P2RU1,P2U, P2U1, P2UR, P2Y2, P2Y2R, ATP receptor; P2U nucleotide receptor; P2Upurinoceptor 1; P2Y purinoceptor 2; purinergic receptor P2Y2;purinoceptor P2Y2 174 progestagen-associated glycodelin-A; glycodelin-F;glycodelin- PAEP endometrial protein (placental S;progesterone-associated protein 14, pregnancy- endometrial proteinassociated endometrial alpha- 2-globulin, alpha uterine protein) 175paired box gene 4 Pax4 (transcription factor) - paired PAX4 domain gene4 176 pre-B-cell colony enhancing visfatin; nicotinamide PBEF1 factor 1phosphoribosyltransferase 177 phosphoenolpyruvate PEPCK1; PEPcarboxykinase; PCK1 carboxykinase 1 (PEPCK1) phosphopyruvatecarboxylase; phosphoenolpyruvate carboxylase 178 proprotein convertaseproprotein convertase 1 (PC1, PC3, PCSK1 subtilisin/kexin type 1 PCSK1,cleaves pro-insulin) 179 placental growth factor, placental growthfactor - PLGF, PIGF-2 PGF vascular endothelial growth factor-relatedprotein 180 phosphoinositide-3-kinase, PI3K, p110-alpha, PI3-kinase p110PIK3CA catalytic, alpha polypeptide subunit alpha; PtdIns-3-kinase p110;phosphatidylinositol 3-kinase, catalytic, 110-KD, alpha;phosphatidylinositol 3- kinase, catalytic, alpha polypeptide;phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit, alphaisoform 181 phosphoinositide-3-kinase, phophatidylinositol 3-kinase;PIK3R1 regulatory subunit 1 (p85 alpha) phosphatidylinositol 3-kinase,regulatory, 1; phosphatidylinositol 3- kinase-associated p-85 alpha;phosphoinositide-3-kinase, regulatory subunit, polypeptide 1 (p85alpha); phosphatidylinositol 3-kinase, regulatory subunit, polypeptide 1(p85 alpha) 182 phospholipase A2, group XIIA PLA2G12, group XII secretedPLA2G12A phospholipase A2; group XIIA secreted phospholipase A2 183phospholipase A2, group IID phospholipase A2, secretory - PLA2G2DSPLASH, sPLA2S, secretory phospholipase A2s 184 plasminogen activator,tissue tissue Plasminogen Activator (tPA), T- PLAT PA, TPA, alteplase;plasminogen activator, tissue type; reteplase; t- plasminogen activator;tissue plasminogen activator (t-PA) 185 patatin-like phospholipaseAdipose tissue lipase, ATGL - ATGL, PNPLA2 domain containing 2 TTS-2.2,adipose triglyceride lipase; desnutrin; transport-secretion protein 2.2;triglyceride hydrolase 186 proopiomelanocortin proopiomelanocortin -beta-LPH; beta- POMC (adrenocorticotropin/beta- MSH; alpha-MSH;gamma-LPH; lipotropia/alpha-melanocyte gamma-MSH; corticotropin; beta-stimulating hormone/beta- endorphin; met-enkephalia; lipotropinmelanocyte stimulating beta; lipotropin gamma; melanotropinhormone/beta-endorphin) beta; N-terminal peptide; melanotropin alpha;melanotropin gamma; pro- ACTH-endorphin; adrenocorticotropin;pro-opiomelanocortin; corticotropin- lipotrophin; adrenocorticotropichormone; alpha-melanocyte- stimulating hormone; corticotropin-likeintermediary peptide 187 paraoxonase 1 ESA, PON, paraoxonase - ESA, PON,PON1 Paraoxonase Paraoxonase 188 peroxisome proliferative Peroxisomeproliferator-activated PPARA activated receptor, alpha receptor (PPAR),NR1C1, PPAR, hPPAR, PPAR alpha 189 peroxisome proliferative Peroxisomeproliferator-activated PPARD activated receptor, delta receptor (PPAR),FAAR, NR1C2, NUC1, NUCI, NUCII, PPAR-beta, PPARB, nuclear hormonereceptor 1, PPAR Delta 190 peroxisome proliferative Peroxisomeproliferator-activated PPARG activated receptor, gamma receptor (PPAR),HUMPPARG, NR1C3, PPARG1, PPARG2, PPAR gamma; peroxisome proliferativeactivated receptor gamma; peroxisome proliferator activated-receptorgamma; peroxisome proliferator-activated receptor gamma 1; ppar gamma2191 peroxisome proliferative Pgc1 alpha; PPAR gamma coactivator-PPARGC1A activated receptor, gamma, 1; ligand effect modulator-6; PPARcoactivator 1 gamma coactivator variant form 3 192 protein phosphatase1, PP1G, PPP1R3, protein phosphatase PPP1R3A regulatory (inhibitor)subunit 3A 1 glycogen-associated regulatory (glycogen and sarcoplasmicsubunit; protein phosphatase 1 reticulum binding subunit,glycogen-binding regulatory subunit 3; skeletal muscle) proteinphosphatase type-1 glycogen targeting subunit; serine/threonine specificprotein phosphatase; type-1 protein phosphatase skeletal muscle glycogentargeting subunit 193 protein phosphatase, 2A, protein phosphatase 2A -PP2A, PR53, PPP2R4 regulatory subunit B′ (PR 53) PTPA, PP2A, subunit B′;phosphotyrosyl phosphatase activator; protein phosphatase 2A, regulatorysubunit B′ 194 protein kinase, AMP-activated, on list as adenosinemonophosphate PRKAB1 beta 1 non-catalytic subunit kinase?- AMPK, HAMPKb,5′-AMP- activated protein kinase beta-1 subunit; AMP-activated proteinkinase beta 1 non-catalytic subunit; AMP- activated protein kinase betasubunit; AMPK beta-1 chain; AMPK beta 1; protein kinase, AMP-activated,noncatalytic, beta-1 195 protein kinase, cAMP- PKA (kinase) - PKACA, PKAC-alpha; PRKACA dependent, catalytic, alpha cAMP-dependent proteinkinase catalytic subunit alpha; cAMP- dependent protein kinase catalyticsubunit alpha, isoform 1; protein kinase A catalytic subunit 196 proteinkinase C, epsilon PKC-epsilon - PKCE, nPKC-epsilon PRKCE 197 proteasome(prosome, Bridge-1; homolog of rat Bridge 1; 26S PSMD9 macropain) 26Ssubunit, non- proteasome regulatory subunit p27; ATPase, 9 (Bridge-1)proteasome 26S non-ATPase regulatory subunit 9 198 prostaglandin Esynthase mPGES - MGST-IV, MGST1-L1, PTGES MGST1L1, PGES, PIG12, PP102,PP1294, TP53112 Other Designations: MGST-like 1; glutathioneS-transferase 1-like 1; microsomal glutathione S-transferase 1-like 1;p53-induced apoptosis protein 12; p53-induced gene 12; tumor protein p53inducible protein 12 199 prostaglandin-endoperoxide Cyclo-oxygenase-2(COX-2) - COX-2, PTGS2 synthase 2 (prostaglandin G/H COX2, PGG/HS,PGHS-2, PHS-2, synthase and cyclooxygenase) hCox-2, cyclooxygenase 2b;prostaglandin G/H synthase and cyclooxygenase; prostaglandin-endoperoxide synthase 2 200 protein tyrosine phosphatase, PTPMT1 - PLIP,PNAS-129, NB4 PTPMT1 mitochondrial 1 apoptosis/differentiation relatedprotein; PTEN-like phosphatase 201 Peptide YY PYY1 PYY 202 retinolbinding protein 4, plasma RBP4; retinol-binding protein 4, RBP4 (RBP4)plasma; retinol-binding protein 4, interstitial 203 regeneratingislet-derived 1 regenerating gene product (Reg); REGIA alpha (pancreaticstone protein, protein-X; lithostathine 1 alpha; pancreatic threadprotein) pancreatic thread protein; regenerating protein I alpha; isletcells regeneration factor; pancreatic stone protein, secretory; islet ofLangerhans regenerating protein 204 resistin resistin - ADSF, FIZZ3,RETNI, RSTN, RETN XCP1, C/EBP-epsilon regulated myeloid-specificsecreted cysteine-rich protein precursor 1; found in inflammatory zone 3205 ribosomal protein S6 kinase, S6-kinase 1 - IIU-1, RSK, RSK1, S6K-RPS6KAI 90 kDa, polypeptide 1 alpha 1, (ribosomal protein S6 kinase, 90kD, polypeptide 1); p90-RSK 1; ribosomal protein S6 kinase alpha 1;ribosomal protein S6 kinase, 90 kD, 1; ribosomal protein S6 kinase, 90kD, polypeptide 1 206 Ras-related associated with RAD, RAD1, REM3, RAS(RAD and RRAD Diabetes GEM) like GTP binding 3 207 serum amyloid A1Serum Amyloid A (SAA), PIG4, SAA, SAA1 TP53I4, tumor protein p53inducible protein 4 208 selectin E (endothelial adhesion E-selectin,CD62E, ELAM, ELAM1, SELE molecule 1) ESEL, LECAM2, leukocyte endothelialcell adhesion molecule 2; selectin E, endothelial adhesion molecule 1209 selectin P (granule membrane CD62, CD62P, FLJ45155, GMP140, SELPprotein 140 kDa, antigen CD62) GRMP, PADGEM, PSEL; antigen CD62;granulocyte membrane protein; selectin P; selectin P (granule membraneprotein 140 kD, antigen CD62) 210 serpin peptidase inhibitor, cladecorticosteroid-binding globulin; SERPINA6 A (alpha-1 antiproteinase,transcortin; corticosteroid binding antitrypsin), member 6 globulin;serine (or cysteine) proteinase inhibitor, clade A (alpha-1antiproteinase, antitrypsin), member 6 211 serpin peptidase inhibitor,clade plasminogen activator inhibitor-1 - PAI, SERPINE1 E (nexin,plasminogen activator PAI-1, PAIL, PLANH1, plasminogen inhibitor type1), member 1 activator inhibitor, type I; plasminogen activatorinhibitor-1; serine (or cysteine) proteinase inhibitor, clade E (nexin,plasminogen activator inhibitor type 1), member 1 212serum/glucocorticoid regulated Serum/Glucocorticoid Regulated SGK kinaseKinase 1 - SGK1, serine/threonine protein kinase SGK; serum andglucocorticoid regulated kinase 213 sex hormone-binding globulin sexhormone-binding globulin (SHBG) - SHBG ABP, Sex hormone-binding globulin(androgen binding protein) 214 thioredoxin interacting protein Sirt1;SIR2alpha; sir2-like 1; sirtuin SIRT1 type 1; sirtuin (silent matingtype information regulation 2, S. cerevisiae, homolog) 1 215 solutecarrier family 2, member glucose transporter 10 (GLUT10); ATS SLC2A10 10216 solute carrier family 2, member 2 glucose transporter 2 (GLUT2)SLC2A2 217 solute carrier family 2, member 4 glucose transporter 4(GLUT4) SLC2A4 218 solute carrier family 7 (cationic ERR - ATRC1, CAT-1,ERR, HCAT1, SLC7A1 amino acid transporter, y+ REC1L, amino acidtransporter, system), member 1(ERR) cationic 1; ecotropic retroviralreceptor 219 SNF1-like kinase 2 Sik2; salt-inducible kinase 2; salt-SNF1LK2 inducible serine/threonine kinase 2 220 suppressor of cytokineCIS3, Cish3, SOCS-3, SSI-3, SSI3, SOCS3 signaling 3 STAT induced STATinhibitor 3; cytokine-induced SH2 protein 3 221 v-src sarcoma(Schmidt-Ruppin ASV, SRC1, c-SRC, p60-Src, proto- SRC A-2) viraloncogene homolog oncogene tyrosine-protein kinase (avian) SRC;protooncogene SRC, Rous sarcoma; tyrosine kinase pp60c-src;tyrosine-protein kinase SRC-1 222 sterol regulatory element sterolregulatory element-binding SREBF1 binding transcription factor 1 protein1c (SREBP-1c) 223 solute carrier family 2, member 4 SMST,somatostatin-14, somatostatin- SST 28 224 somatostatin receptor 2somatostatin receptor subtype 2 SSTR2 225 somatostatin receptor 5somatostatin receptor 5 - somatostatin SSTR5 receptor subtype 5 226transcription factor 1, hepatic; HNFIα; albumin proximal factor; TCF1LF-B1, hepatic nuclear factor hepatic nuclear factor 1; maturity onset(HNF1) Diabetes of the young 3; Interferon production regulator factor(HNF1) 227 transcription factor 2, hepatic; hepatocyte nuclear factor2 - FJHN, TCF2 LF-B3; variant hepatic nuclear HNF1B, HNF1beta, HNF2,LFB3, factor MODY5, VHNF1, transcription factor 2 228 transcriptionfactor 7-like 2 (T- TCF7L2 - TCF-4, TCF4 TCF7L2 cell specific, HMG-box)229 transforming growth factor, beta TGF-beta: TGF-beta 1 protein; TGFB11 (Camurati-Engelmann diaphyseal dysplasia 1, progressive; disease)transforming growth factor beta 1; transforming growth factor, beta 1;transforming growth factor-beta 1, CED, DPD1, TGFB 230 transglutaminase2 (C TG2, TGC, C polypeptide; TGase C; TGM2 polypeptide,protein-glutamine- TGase-H; protein-glutamine-gamma-gamma-glutamyltransferase) glutamyltransferase; tissue transglutaminase;transglutaminase 2; transglutaminase C 231 thrombospondin 1thrombospondin - THBS, TSP, TSP1, THBS1 thrombospondin-1p180 232thrombospondin, type I, domain TMTSP, UNQ3010, thrombospondin THSD1containing 1 type I domain-containing 1; thrombospondin, type I, domain1; transmembrane molecule with thrombospondin module 233 TIMPmetallopeptidase inhibitor CSC-21K; tissue inhibitor of TIMP2metalloproteinase 2; tissue inhibitor of metalloproteinase 2 precursor;tissue inhibitor of metalloproteinases 2 234 tumor necrosis factor (TNFTNF-alpha (tumour necrosis factor- TNF superfamily, member 2) alpha) -DIF, TNF-alpha, TNFA, TNFSF2, APC1 protein; TNF superfamily, member 2;TNF, macrophage-derived; TNF, monocyte- derived; cachectin; tumornecrosis factor alpha 235 tumor necrosis factor receptor MGC29565, OCIF,OPG, TR1; TNFRSF11B superfamily, member 11b osteoclastogenesisinhibitory factor; (osteoprotegerin) osteoprotegerin 236 tumor necrosisfactor receptor tumor necrosis factor receptor 1 gene TNFRSF1Asuperfamily, member 1A R92Q polymorphism - CD120a, FPF, TBP1, TNF-R,TNF-R-I, TNF-R55, TNFAR, TNFR1, TNFR55, TNFR60, p55, p55-R, p60, tumornecrosis factor binding protein 1; tumor necrosis factor receptor 1;tumor necrosis factor receptor type 1; tumor necrosis factor- alphareceptor 237 tumor necrosis factor receptor soluble necrosis factorreceptor - TNFRSF1B superfamily, member 1B CD120b, TBPII, TNF-R-II,TNF-R75, TNFBR, TNFR2, TNFR80, p75, p75TNFR, p75 TNF receptor; tumornecrosis factor beta receptor; tumor necrosis factor binding protein 2;tumor necrosis factor receptor 2 238 tryptophan hydroxylase 2 enzymesynthesizing serotonin; TPH2 neuronal tryptophan hydroxylase, NTPH 239thyrotropin-releasing hormone thyrotropin-releasing hormone TRH 240transient receptor potential vanilloid receptor 1 - VR1, capsaicin TRPV1cation channel, subfamily V, receptor; transient receptor potentialmember 1 vanilloid 1a; transient receptor potential vanilloid 1b;vanilloid receptor subtype 1, capsaicin receptor; transient receptorpotential vanilloid subfamily 1 (TRPV1) 241 thioredoxin interactingprotein thioredoxin binding protein 2; TXNIP upregulated by1,25-dihydroxyvitamin D-3 242 thioredoxin reductase 2 TR; TR3; SELZ;TRXR2; TR-BETA; TXNRD2 selenoprotein Z; thioredoxin reductase 3;thioredoxin reductase beta 243 urocortin 3 (stresscopin) archipelin,urocortin III, SCP, SPC, UCN3 UCNIII, stresscopin; urocortin 3 244uncoupling protein 2 UCPH, uncoupling protein 2; UCP2 (mitochondrial,proton carrier) uncoupling protein-2 245 upstream transcription factor 1major late transcription factor 1 USF1 246 urotensin 2 PRO1068, U-II,UCN2, UII UTS2 247 vascular cell adhesion molecule 1 (soluble) vascularcell adhesion VCAM1 molecule-1, CD106, INCAM-100, CD106 antigen, VCAM-1248 vascular endothelial growth VEGF - VEGFA, VPF, vascular VEGF factorendothelial growth factor A; vascular permeability factor 249 vimentinvimentin VIM 250 vasoactive intestinal peptide vasoactive intestinalpeptide - PHM27 VIP 251 vasoactive intestinal peptide vasoactiveintestinal peptide receptor 1 - VIPR1 receptor 1 HVR1, II, PACAP-R-2,RCD1, RDC1, VIPR, VIRG, VPAC1, PACAP type II receptor; VIP receptor,type I; pituitary adenylate cyclase activating polypeptide receptor,type II 252 vasoactive intestinal peptide Vasoactive Intestinal PeptideReceptor VIPR2 receptor 2 2 - VPAC2 253 von Willebrand factor vonWillebrand factor, F8VWF, VWD, VWF coagulation factor VIII VWF 254Wolfram syndrome I (wolframin) DFNA14, DFNA38, DFNA6, WFS1 DIDMOAD,WFRS, WFS, WOLFRAMIN 255 X-ray repair complementing Ku autoantigen, 70kDa; Ku XRCC6 defective repair in Chinese autoantigen p70 subunit;thyroid-lupus hamster cells 6 autoantigen p70; CTC box binding factor 75kDa subunit; thyroid autoantigen 70 kD (Ku antigen); thyroid autoantigen70 kDa (Ku antigen); ATP- dependent DNA helicase II, 70 kDa subunit 256c-peptide c-peptide, soluble c-peptide SCp 257 cortisol cortisol -hydrocortisone is the synthetic form 258 vitamin D3 vitamin D3 259estrogen estrogen 260 estradiol estradiol 261 digitalis-like factordigitalis-like factor 262 oxyntomodulin oxyntomodulin 263dehydroepiandrosterone sulfate dehydroepiandrosterone sulfate (DHEAS)(DHEAS) 264 serotonin (5-hydroxytryptamine) serotonin(5-hydroxytryptamine) 265 anti-CD38 autoantibodies anti-CD38autoantibodies 266 gad65 autoantibody gad65 autoantibody epitopes 267Proinsulin Repeat “Proinsulin” here? PROINS 268 endoglin END; ORW; HHT1;ORW1; CD105; ENG FLJ41744; RP11-228B15.2 269 interleukin 2 receptor,beta CD122; P70-75; CD122 antigen; IL2RB OTTHUMP00000028799; highaffinity IL-2 receptor beta subunit 270 insulin-like growth factor IBP2;IGF-BP53 IGFBP2 binding protein 2 271 insulin-like growth factor 1CD221; IGFIR; ITK13; MGC142170; IGF1R receptor MGC142172; MGC18216 272fructosamine Repeat “fructosamine” here?

TABLE 8 # Clinical Parameter (CPs) 272 Age (AGE) 273 Body Mass Index(BMI) 274 Diastolic Blood Pressure (DBP) 275 Family History (FHX) orFHX1-one parent with Diabetes; and FHX2-two parents with Diabetes) 276Gestational Diabetes Mellitus (GDM), Past 277 Height (HT) 278 HipCircumference (Hip) 279 Race (RACE) 280 Sex (SEX) 281 Sysolic BloodPressure (SBP) 282 Wast Circumference (Waist) 283 Weight (WT)(and other combinations thereof, including Waist to Hip Ratio (WHr)).

TABLE 9 # Traditional Laboratory Risk Factors (TLRFs) 284 Cholesteral(CHOL) 285 Glucose (fasting plasma glucose (FPG/Glucose or with oralglucose tolerance test (OGTT) 286 HBA1c (Glycosylated Hemoglobin(HBA1/BHA1C) 287 High Desnity Lipoprotein (HDL/HDLC) 288 Low DensityLipoprotein (LDL/LDLC) 289 Very Low Density Lipoprotein (VLDLC) 290Triglycerides (TRIG)

One skilled in the art will note that the above listed ALLDBRISK markers(“ALLDBRISKS”) come from a diverse set of physiological and biologicalpathways, including many which are not commonly accepted to be relatedto Diabetes. These groupings of different ALLDBRISK markers, even withinthose high significance segments, may presage differing signals of thestage or rate of the progression of the disease. Such distinct groupingsof ALLDBRISK markers may allow a more biologically detailed andclinically useful signal from the ALLDBRISK markers as well asopportunities for pattern recognition within the ALLDBRISK algorithmscombining the multiple ALLDBRISK signals.

The present invention concerns, in one aspect, a subset of ALLDBRISKmarkers; other ALLDBRISKS and even biomarkers which are not listed inthe above Table 7, but related to these physiological and biologicalpathways, may prove to be useful given the signal and informationprovided from these studies. To the extent that other biomarker pathwayparticipants (i.e., other biomarker participants in common pathways withthose biomarkers contained within the list of ALLDBRISKS in the aboveTable 7) are also relevant pathway participants in pre-Diabetes,Diabetes, or a pre-diabetic condition, they may be functionalequivalents to the biomarkers thus far disclosed in Table 7.

These other pathway participants are also considered ALLDBRISKS in thecontext of the present invention, provided they additionally sharecertain defined characteristics of a good biomarker, which would includeboth involvement in the herein disclosed biological processes and alsoanalytically important characteristics such as the bioavailability ofsaid biomarkers at a useful signal-to-noise ratio, and in a usefulsample matrix such as blood serum. Such requirements typically limit thediagnostic usefulness of many members of a biological pathway, andfrequently occurs only in pathway members that constitute secretorysubstances, those accessible on the plasma membranes of cells, as wellas those that are released into the serum upon cell death, due toapoptosis or for other reasons such as endothelial remodeling or othercell turnover or cell necrotic processes, whether or not they arerelated to the disease progression of pre-Diabetes, a pre-diabeticcondition, and Diabetes. However, the remaining and future biomarkersthat meet this high standard for ALLDBRISKS are likely to be quitevaluable.

Furthermore, other unlisted biomarkers will be very highly correlatedwith the biomarkers listed as ALLDBRISKS in Table 7 (for the purpose ofthis application, any two variables will be considered to be “veryhighly correlated” when they have a correlation (R) of 0.4 or greater).The present invention encompasses such functional and statisticalequivalents to the aforementioned ALLDBRISKS. Furthermore, thestatistical utility of such additional ALLDBRISKS is substantiallydependent on the cross-correlation between multiple biomarkers and anynew biomarkers will often be required to operate within a panel in orderto elaborate the meaning of the underlying biology.

One or more, preferably two or more of the listed ALLDBRISKS can bedetected in the practice of the present invention. For example, two (2),three (3), four (4), five (5), ten (10), fifteen (15), twenty (20),forty (40), fifty (50), seventy-five (75), one hundred (100), onehundred and twenty five (125), one hundred and fifty (150), one hundredand seventy-five (175), two hundred (200), two hundred and ten (210),two hundred and twenty (220), two hundred and thirty (230), two hundredand forty (240), two hundred and fifty (250), two hundred and sixty(260) or more ALLDBRISKS can be detected. In some aspects, allALLDBRISKS listed herein can be detected. Preferred ranges from whichthe number of ALLDBRISKS can be detected include ranges bounded by anyminimum selected from between one and all known ALLDBRISKS, particularlyup to two, five, ten, twenty, twenty-five, thirty, forty, fifty,seventy-five, one hundred, one hundred and twenty five, one hundred andfifty, one hundred and seventy-five, two hundred, two hundred and ten,two hundred and twenty, two hundred and thirty, two hundred and forty,two hundred and fifty, paired with any maximum up to the total knownALLDBRISKS, particularly up to five, ten, twenty, fifty, andseventy-five. Particularly preferred ranges include two to five (2-5),two to ten (2-10), two to fifty (2-50), two to seventy-five (2-75), twoto one hundred (2-100), five to ten (5-10), five to twenty (5-20), fiveto fifty (5-50), five to seventy-five (5-75), five to one hundred(5-100), ten to twenty (10-20), ten to fifty (10-50), ten toseventy-five (10-75), ten to one hundred (10-100), twenty to fifty(20-50), twenty to seventy-five (20-75), twenty to one hundred (20-100),fifty to seventy-five (50-75), fifty to one hundred (50-100), onehundred to one hundred and twenty-five (100-125), one hundred andtwenty-five to one hundred and fifty (125-150), one hundred and fifty toone hundred and seventy five (150-175), one hundred and seventy-five totwo hundred (175-200), two hundred to two hundred and ten (200-210), twohundred and ten to two hundred and twenty (210-220), two hundred andtwenty to two hundred and thirty (220-230), two hundred and thirty totwo hundred and forty (230-240), two hundred and forty to two hundredand fifty (240-250), two hundred and fifty to two hundred and sixty(250-260), and two hundred and sixty to more than two hundred and sixty(260+).

In some variation of the invention, any integer number of ALLDBRISK from1 to 20 is combined with an integer number of lipid metabolite biomarkerfrom 1 to 20, as well as any integer number from 1 to 10 of otherfactors described herein, including glucose, clinical parameters andtraditional risk factors.

Construction of Clinical Algorithms

Any formula may be used to combine ALLDBRISK results, lipid metabolites,traditional risk factors, glucose and other parameters described herein,into indices useful in the practice of the invention. As indicatedabove, and without limitation, such indices may indicate, among thevarious other indications, the probability, likelihood, absolute orrelative risk, time to or rate of conversion from one to another diseasestate, or make predictions of future biomarkers measurements of Diabetessuch as Glucose or HBA1c used for Diabetes in the diagnosis of the frankdisease. This may be for a specific time period or horizon, or forremaining lifetime risk, or simply be provided as an index relative toanother reference subject population.

Although various preferred formulas are described here, several othermodel and formula types beyond those mentioned herein and in thedefinitions above are well known to one skilled in the art. The actualmodel type or formula used may itself be selected from the field ofpotential models based on the performance and diagnostic accuracycharacteristics of its results in a training population. The specificsof the formula itself may commonly be derived from ALLDBRISK, lipidmetabolites and other results in the relevant training population. Amongother uses, such formula may be intended to map the feature spacederived from one or more ALLDBRISK, lipid metabolites and other inputsto a set of subject classes (e.g., useful in predicting class membershipof subjects as normal, pre-Diabetes, Diabetes), to derive an estimationof a probability function of risk using a Bayesian approach (e.g., therisk of Diabetes), or to estimate the class-conditional probabilities,then use Bayes' rule to produce the class probability function as in theprevious case.

Preferred formulas include the broad class of statistical classificationalgorithms, and in particular the use of discriminant analysis. The goalof discriminant analysis is to predict class membership from apreviously identified set of features. In the case of lineardiscriminant analysis (LDA), the linear combination of features isidentified that maximizes the separation among groups by some criteria.Features can be identified for LDA using an Eigengene-based approachwith different thresholds (ELD A) or a stepping algorithm based on amultivariate analysis of variance (MANOVA). Forward, backward, andstepwise algorithms can be performed that minimize the probability of noseparation based on the Hotelling-Lawley statistic.

Eigengene-based Linear Discriminant Analysis (ELD A) is a featureselection technique developed by Shen et al. (2006). The formula selectsfeatures (e.g. biomarkers) in a multivariate framework using a modifiedeigenanalysis to identify features associated with the most importanteigenvectors. “Important” is defined as those eigenvectors that explainthe most variance in the differences among samples that are trying to beclassified relative to some threshold.

A support vector machine (SVM) is a classification formula that attemptsto find a hyperplane that separates two classes. This hyperplanecontains support vectors, data points that are exactly the margindistance away from the hyperplane. In the likely event that noseparating hyperplane exists in the current dimensions of the data, thedimensionality is expanded greatly by projecting the data into largerdimensions by taking non-linear functions of the original variables(Venables and Ripley, 2002). Although not required, filtering offeatures for an SVM often improves prediction. Features (e.g.,biomarkers) can be identified for an SVM using a non-parametricKruskal-Wallis (KW) test to select the best univariate features. Arandom forest (R F, Breiman, 2001) or recursive partitioning (RPART,Breiman et al., 1984) can also be used separately or in combination toidentify biomarker combinations that are most important. Both KW and RFrequire that a number of features be selected from the total. RPARTcreates a single classification tree using a subset of availablebiomarkers.

Other formulas may be used in order to pre-process the results ofindividual ALLDBRISK, lipid metabolite, and other measurements into morevaluable forms of information, prior to their presentation to thepredictive formula. Most notably, normalization of biomarker results,using either common mathematical transformations such as logarithmic orlogistic functions, as normal or other distribution positions, inreference to a population's mean values, etc. are all well known tothose skilled in the art. Of particular interest are a set ofnormalizations based on Clinical Parameters such as age, gender, race,or sex, where specific formulas are used solely on subjects within aclass or continuously combining a Clinical Parameter as an input. Inother cases, analyte based biomarkers can be combined into calculatedvariables (much as BMI is a calculation using Height and Weight) whichare subsequently presented to a formula.

In addition to the individual parameter values of one subjectpotentially being normalized, an overall predictive formula for allsubjects, or any known class of subjects, may itself be recalibrated orotherwise adjusted based on adjustment for a population's expectedprevalence and mean biomarker parameter values, according to thetechnique outlined in D'Agostino et al. (2001) JAMA 286:180-187, orother similar normalization and recalibration techniques. Suchepidemiological adjustment statistics may be captured, confirmed,improved and updated continuously through a registry of past datapresented to the model, which may be machine readable or otherwise, oroccasionally through the retrospective query of stored samples orreference to historical studies of such parameters and statistics.Additional examples that may be the subject of formula recalibration orother adjustments include statistics used in studies by Pepe, M. S. etal., 2004 on the limitations of odds ratios; Cook, N. R., 2007 relatingto ROC curves; and Vasan, R. S., 2006 regarding biomarkers ofcardiovascular disease.

Finally, the numeric result of a classifier formula itself may betransformed postprocessing by its reference to an actual clinicalpopulation and study results and observed endpoints, in order tocalibrate to absolute risk and provide confidence intervals for varyingnumeric results of the classifier or risk formula. An example of this isthe presentation of absolute risk, and confidence intervals for thatrisk, derived using an actual clinical study, chosen with reference tothe output of the recurrence score formula in the Oncotype Dx product ofGenomic Health, Inc. (Redwood City, Calif.). A further modification isto adjust for smaller sub-populations of the study based on the outputof the classifier or risk formula and defined and selected by theirClinical Parameters, such as age or sex.

Summary of Algorithm Development Process and Application of Algorithms

FIG. 7 is a flow diagram of an example method 200 for developing a modelwhich may be used to evaluate a risk of a person, or group of people,for developing a diabetic condition. The method 200 may be implementedusing the example computing system environment 100 of FIG. 6 and will beused to explain the operation of the environment 100. However, it shouldbe recognized that the method 200 could be implemented by a systemdifferent than the computing system environment 100. At a block 202,biomarker data from a representative population, as has been describedherein, is obtained from a data storage device, such as the systemmemory 130, an internal or external database, or other computer storagemedia. The biomarker data may be initially derived through a variety ofmeans, including prospective (longitudinal) studies to involvingobservations of the representative population over a period of time,retrospective studies of samples of a representative population thatqueries the samples and/or from a retrospective epidemiological datastorage containing the results from previous studies, such as an NIHdatabase. The biomarker data may be derived from a single study ormultiple studies, and generally includes data pertaining to the desiredindication and endpoint of the representative population, includingvalues of the biomarkers described herein, clinical annotations (whichmay include endpoints), and most particularly the desired endpoints fortraining an algorithm for use in the invention, across many subjects.

At a block 204, the representative population data set is prepared asneeded to meet the requirements of the model or analysis that will beused for biomarker selection, as described below. For example, data setpreparation may include preparing the biomarker values from each subjectwithin the representative population, or a chosen subset thereof.However, the raw biomarker data alone may not be entirely useful for thepurposes of model training. As such, various data preparation methodsmay be used to prepare the data, such as gap fill techniques (e.g.,nearest neighbor interpolation or other pattern recognition), qualitychecks, data combination using of various formulas (e.g., statisticalclassification algorithms), normalization and/or transformations, suchas logarithmic functions to change the distribution of data to meetmodel requirements (e.g., base 10, natural log, etc.). Again, theparticular data preparation procedures are dependent upon the model ormodels that will be trained using the representative population data.The particular data preparation techniques for various different modeltypes are known, and need not be described further.

At a block 206, the particular biomarkers are selected to besubsequently used in the training of the model used to evaluate a riskof developing a diabetic condition. Biomarker selection may involveutilizing a selection model to validate the representative populationdata set and selecting the biomarker data from the data set thatprovides the most reproducible results. Examples of data set validationmay include, but are not limited to, cross-validation and bootstrapping.From the marker selection, the model to be used in evaluating a risk ofdeveloping a diabetic condition may be determined and selected. However,it is noted that not all models provide the same results with the samedata set. For example, different models may utilize different numbers ofbiomarkers and produce different results, thereby adding significance tothe combination of biomarkers on the selected model. Accordingly,multiple selection models may be chosen and utilized with therepresentative population data set, or subsets of the data set, in orderto identify the optimal model for risk evaluation. Examples of theparticular models, including statistical models, algorithms, etc., whichmay be used for selecting the biomarkers have been described above.

For each selection model used with the data set, or subset thereof, thebiomarkers are selected based on each biomarker's statisticalsignificance in the model. When input to each model, the biomarkers areselected based on various criteria for statistical significance, and mayfurther involve cumulative voting and weighting. Tests for statisticalsignificance may include exit tests and analysis of variance (ANOVA).The model may include classification models (e.g., LDA, logisticregression, SVM, RF, tree models, etc.) and survival models (e.g., Cox),many examples of which have been described above.

It is noted that while biomarkers may be applied individually to eachselection model to identify the statistically significant biomarkers, insome instances individual biomarkers alone may not be fully indicativeof a risk for a diabetic condition, in which case combinations ofbiomarkers may be applied to the selection model. For example, ratherthan utilizing univariate biomarker selection, multivariate biomarkerselection may be utilized. That is, a biomarker may not be a goodindicator when used as a univariate input to the selection model, butmay be a good indicator when used in combination with other biomarkers(i.e., a multivariate input to the model), because each marker may bringadditional information to the combination that would not be indicativeif taken alone.

At a block 208, the model to be used for evaluating risk is selected,trained and validated. In particular, leading candidate models may beselected based on one or more performance criteria, examples of whichhave been described above. For example, from using the data set, or datasubsets, with various models, not only are the models used to determinestatistically significant biomarkers, but the results may be used toselect the optimal models along with the biomarkers. As such, theevaluation model used to evaluate risk may include one of those used asa selection model, including classification models and survival models.Combinations of models markers, including marker subsets, may becompared and validated in subsets and individual data sets. Thecomparison and validation may be repeated many times to train andvalidate the model and to choose an appropriate model, which is thenused as an evaluation model for evaluating risk of a diabetic condition.

FIG. 8 is a flow diagram of an example method 250 for using a model toevaluate a risk of a subject (e.g., a person, or group of people)developing a diabetic condition. At a block 252, biomarker data from thesubject is obtained from a data storage device, which may be the sameas, or different from, the data storage device discussed above withreference to FIG. 7. The subject biomarker data may be initially derivedthrough a variety of means, including self-reports, physicalexamination, laboratory testing and existing medical records, charts ordatabases. As with the representative population biomarker data at block204 of FIG. 7, the subject biomarker data at block 254 may be preparedusing transforms, logs, combinations, normalization, etc. as neededaccording to the model type selected and trained in FIG. 7. Once thedata has been prepared, at a block 256, the subject biomarker data isinput into the evaluation model, and at a block 258 the evaluation modeloutputs an index value (e.g., risk score, relative risk, time toconversion, etc.). Many examples have been provided herein as to how amodel may be used to evaluate the subject biomarkers and output an indexvalue, e.g., see Example 1.

Modifications for Therapeutic Intervention Panels

A panel of ALLDBRISK, lipid metabolites, glucose and optionally otherparameters can be constructed and formula derived specifically toenhance performance for use also in subjects undergoing therapeuticinterventions, or a separate panel and formula may alternatively be usedsolely in such patient populations. An aspect of the invention is theuse of specific known characteristics of these biomarkers and otherparameters and their changes in such subjects for such panelconstruction and formula derivation. Such modifications may enhance theperformance of various indications noted above in Diabetes prevention,and diagnosis, therapy, monitoring, and prognosis of Diabetes andpre-Diabetes.

Several of the ALLDBRISKS and other biomarkers and parameters disclosedherein are known to those skilled in the art to vary predictably undertherapeutic intervention, whether lifestyle (e.g., diet and exercise),surgical (e.g., bariatric surgery) or pharmaceutical (e.g., one of thevarious classes of drugs mentioned herein or known to modify common riskfactors or risk of Diabetes) intervention. For example, a PubMed searchusing the terms “Adiponectin drug,” will return over 700 references,many with respect to the changes or non-changes in the levels ofadiponectin (ADIPOQ) in subjects treated with various individualDiabetes-modulating agents. Similar evidence of variance undertherapeutic intervention is widely available for many of the biomarkerslisted in Table 7, such as CRP, FGA, INS, LEP, among others. Certain ofthe biomarkers listed, most particularly the Clinical Parameters and theTraditional Laboratory Risk Factors, (and including such biomarkers asGLUCOSE, SBP, DBP, CHOL, HDL, and HBA1c), are traditionally used assurrogate or primary endpoint markers of efficacy for entire classes ofDiabetes-modulating agents, thus most certainly changing in astatistically significant way.

Still others, including genetic biomarkers, such as those polymorphismsknown in the PPARG and INSR (and generally all genetic biomarkers absentsomatic mutation), are similarly known not to vary in their measurementunder particular therapeutic interventions. Such variation may or maynot impact the general validity of a given panel, but will often impactthe index values reported, and may require different marker selection,the formula to be re-optimized or other changes to the practice of theinvention. Alternative model calibrations may also be practiced in orderto adjust the normally reported results under a therapeuticintervention, including the use of manual table lookups and adjustmentfactors.

Such properties of the individual ALLDBRISKS and other parameters canthus be anticipated and exploited to select, guide, and monitortherapeutic interventions. For example, specific ALLDBRISKS and otherparameters may be added to, or subtracted from, the set underconsideration in the construction of the ALLDBRISK panels, based onwhether they are known to vary, or not to vary, under therapeuticintervention. Alternatively, such ALLDBRISKS and other parameters may beindividually normalized or formula recalibrated to adjust for sucheffects according to the above and other means well known to thoseskilled in the art.

Combination with Clinical Parameters

Any of the aforementioned Clinical Parameters, ALLDBRISK biomarkers,lipid metabolite other factors, may be used in the practice of theinvention as an input to a formula or as a pre-selection criteriadefining a relevant population to be measured using a particular paneland formula. As noted above, Clinical Parameters may also be useful inthe biomarker normalization and pre-processing, or in biomarkerselection, panel construction, formula type selection and derivation,and formula result post-processing.

Endpoints of the Invention

One embodiment of the invention is to tailor panels and formulas to thepopulation and end point or use that is intended. For example, thepanels and formulas may used for assessment of subjects for primaryprevention and diagnosis and for secondary prevention and management.For the primary assessment, the panels and formulas may be used forprediction and risk stratification for conditions, for the diagnosis ofdiabetic conditions, for the prognosis of glucose level and rate ofchange and for indication for future diagnosis. For secondary preventionand management, the panels and formulas may be used for prognosis, riskstratification for Diabetes complications. The panels and formulas maybe used for clinical decision support, such as determining whether todefer intervention to next visit, to recommend normal preventivecheck-ups, to recommend increased visit frequency, to recommendincreased testing and to recommend therapeutic intervention. The panelsand formulas may also be useful for intervention in subjects withdiabetic conditions, such as therapeutic selection and response,adjustment and dosing of therapy, monitoring ongoing therapeuticefficiency and indication for change in therapeutic intervention.

The disease endpoints of the invention include Type 1 and Type 2Diabetes Mellitus and other diabetic conditions and pre-diabeticconditions. The panels and formulas may be used to evaluate the currentstatus of the disease endpoints by aiding in the diagnosis of latentType 2 Diabetes Mellitus, and aiding in the determination of severity ofthe Type 2 Diabetes Mellitus and determination of the subclass of Type 2Diabetes Mellitus. The panels and formulas are also useful fordetermining the future status of intervention such as determining theprognosis of future Type 2 Diabetes Mellitus with therapy, interventionand drug therapy. The invention may be tailored to a specificintervention, drug class, therapeutic class or therapy or drug therapyor a combination thereof.

The surrogate endpoints of the invention include measuring HBA1c,glucose (FPG and OGTT), and glucose class (normal glucose tolerance(NGT), IGT, IFG and T2DM). The panels and formulas are useful fordetermining the current status of the surrogate endpoints by diagnosingglucose class with or without fasting. The future status of surrogateendpoints may be determined using the panels and formulas of theinvention such as determination of the prognosis of future glucoseclass. The panels and formulas are also useful for determining thefuture status of intervention such as determination of prognosis offuture glucose class with drug therapy.

The complication endpoints of diabetic conditions include eyeretinopathy, microvascular damage, liver damage, limb amputation andcardiovascular complications to name a few. The panels and formulas maybe used to evaluate the current status of the disease endpoints byaiding in the diagnosis of liver damage. The future status ofcomplication endpoints may be determined using the panels and formulassuch as determination of the prognosis of future retinopathy. The panelsand formulas are also useful for determining the future status ofintervention such as determining the prognosis of future retinopathywith therapy or drug therapy.

Measurement of ALLDBRISKS and Other Biomarkers and Parameters

Biomarkers may be measured in using several techniques designed toachieve more predictable subject and analytical variability. On subjectvariability, many of the above ALLDBRISKS and other biomarkers andparameters are commonly measured in a fasting state, and most commonlyin the morning, providing a reduced level of subject variability due toboth food consumption and metabolism and diurnal variation. Theinvention hereby claims all fasting and temporal-based samplingprocedures using the ALLDBRISKS and other biomarkers and parametersdescribed herein. Pre-processing adjustments of ALLDBRISK and otherbiomarker and parameter results may also be intended to reduce thiseffect.

The actual measurement of levels of the ALLDBRISKS can be determined atthe protein or nucleic acid level using any method known in the art. Forexample, at the nucleic acid level, Northern and Southern hybridizationanalysis, as well as ribonuclease protection assays using probes whichspecifically recognize one or more of these sequences can be used todetermine gene expression. Alternatively, levels of ALLDBRISKS can bemeasured using reverse-transcription-based PCR assays (RT-PCR), e.g.,using primers specific for the differentially expressed sequence ofgenes. Levels of ALLDBRISKS can also be determined at the protein level,e.g., by measuring the levels of peptides encoded by the gene productsdescribed herein, or activities thereof. Such methods are well known inthe art and include, e.g., immunoassays based on antibodies to proteinsencoded by the genes, aptamers or molecular imprints. Any biologicalmaterial can be used for the detection/quantification of the protein orits activity. Alternatively, a suitable method can be selected todetermine the activity of proteins encoded by the biomarker genesaccording to the activity of each protein analyzed.

The ALLDBRISK proteins, polypeptides, mutations, and polymorphismsthereof can be detected in any suitable manner, but is typicallydetected by contacting a sample from the subject with an antibody whichbinds the ALLDBRISK protein, polypeptide, mutation, or polymorphism andthen detecting the presence or absence of a reaction product. Theantibody may be monoclonal, polyclonal, chimeric, or a fragment of theforegoing, as discussed in detail above, and the step of detecting thereaction product may be carried out with any suitable immunoassay. Thesample from the subject is typically a biological fluid as describedabove, and may be the same sample of biological fluid used to conductthe method described above.

Immunoassays carried out in accordance with the present invention may behomogeneous assays or heterogeneous assays. In a homogeneous assay theimmunological reaction usually involves the specific antibody (e.g.,anti-ALLDBRISK protein antibody), a labeled analyte, and the sample ofinterest. The signal arising from the label is modified, directly orindirectly, upon the binding of the antibody to the labeled analyte.Both the immunological reaction and detection of the extent thereof canbe carried out in a homogeneous solution. Immunochemical labels whichmay be employed include free radicals, radioisotopes, fluorescent dyes,enzymes, bacteriophages, or coenzymes.

In a heterogeneous assay approach, the reagents are usually the sample,the antibody, and means for producing a detectable signal. Samples asdescribed above may be used. The antibody can be immobilized on asupport, such as a bead (such as protein A and protein G agarose beads),plate or slide, and contacted with the specimen suspected of containingthe antigen in a liquid phase. The support is then separated from theliquid phase and either the support phase or the liquid phase isexamined for a detectable signal employing means for producing suchsignal. The signal is related to the presence of the analyte in thesample. Means for producing a detectable signal include the use ofradioactive labels, fluorescent labels, or enzyme labels. For example,if the antigen to be detected contains a second binding site, anantibody which binds to that site can be conjugated to a detectablegroup and added to the liquid phase reaction solution before theseparation step. The presence of the detectable group on the solidsupport indicates the presence of the antigen in the test sample.Examples of suitable immunoassays include, but are not limited tooligonucleotides, immunoblotting, immunoprecipitation,immunofluorescence methods, chemiluminescence methods,electrochemiluminescence (ECL) or enzyme-linked immunoassays.

Those skilled in the art will be familiar with numerous specificimmunoassay formats and variations thereof which may be useful forcarrying out the method disclosed herein. See generally E. Maggio,Enzyme-Immunoassay, (1980) (CRC Press, Inc., Boca Raton, Fla.); see alsoU.S. Pat. No. 4,727,022 to Skold et al., titled “Methods for ModulatingLigand-Receptor Interactions and their Application,” U.S. Pat. No.4,659,678 to Forrest et al., titled “Immunoassay of Antigens,” U.S. Pat.No. 4,376,110 to David et al., titled “Immunometric Assays UsingMonoclonal Antibodies,” U.S. Pat. No. 4,275,149 to Litman et al., titled“Macromolecular Environment Control in Specific Receptor Assays,” U.S.Pat. No. 4,233,402 to Maggio et al., titled “Reagents and MethodEmploying Channeling,” and U.S. Pat. No. 4,230,767 to Boguslaski et al.,titled “Heterogeneous Specific Binding Assay Employing a Coenzyme asLabel.”

Antibodies can be conjugated to a solid support suitable for adiagnostic assay (e.g., beads such as protein A or protein G agarose,microspheres, plates, slides or wells formed from materials such aslatex or polystyrene) in accordance with known techniques, such aspassive binding. Antibodies as described herein may likewise beconjugated to detectable labels or groups such as radiolabels (e.g.,35S, 1251, 1311), enzyme labels (e.g., horseradish peroxidase, alkalinephosphatase), and fluorescent labels (e.g., fluorescein, Alexa, greenfluorescent protein, rhodamine) in accordance with known techniques.

Antibodies can also be useful for detecting post-translationalmodifications of ALLDBRISK proteins, polypeptides, mutations, andpolymorphisms, such as tyrosine phosphorylation, threoninephosphorylation, serine phosphorylation, glycosylation (e.g., O-GlcNAc).Such antibodies specifically detect the phosphorylated amino acids in aprotein or proteins of interest, and can be used in immunoblotting,immunofluorescence, and ELISA assays described herein. These antibodiesare well-known to those skilled in the art, and commercially available.Post-translational modifications can also be determined using metastableions in reflector matrix-assisted laser desorption ionization-time offlight mass spectrometry (MALDI-TOF) (Wirth, U. et al. (2002) Proteomics2(10): 1445-51).

For ALLDBRISK proteins, polypeptides, mutations, and polymorphisms knownto have enzymatic activity, the activities can be determined in vitrousing enzyme assays known in the art. Such assays include, withoutlimitation, kinase assays, phosphatase assays, reductase assays, amongmany others. Modulation of the kinetics of enzyme activities can bedetermined by measuring the rate constant KM using known algorithms,such as the Hill plot, Michaelis-Menten equation, linear regressionplots such as Lineweaver-Burk analysis, and Scatchard plot.

Using sequence information provided by the database entries for theALLDBRISK sequences, expression of the ALLDBRISK sequences can bedetected (if present) and measured using techniques well known to one ofordinary skill in the art. For example, sequences within the sequencedatabase entries corresponding to ALLDBRISK sequences, or within thesequences disclosed herein, can be used to construct probes fordetecting ALLDBRISK RNA sequences in, e.g., Northern blot hybridizationanalyses or methods which specifically, and, preferably, quantitativelyamplify specific nucleic acid sequences. As another example, thesequences can be used to construct primers for specifically amplifyingthe ALLDBRISK sequences in, e.g., amplification-based detection methodssuch as reverse-transcription-based polymerase chain reaction (RT-PCR).When alterations in gene expression are associated with geneamplification, deletion, polymorphisms, and mutations, sequencecomparisons in test and reference populations can be made by comparingrelative amounts of the examined DNA sequences in the test and referencecell populations.

Expression of the genes disclosed herein can be measured at the RNAlevel using any method known in the art. For example, Northernhybridization analysis using probes which specifically recognize one ormore of these sequences can be used to determine gene expression.Alternatively, expression can be measured usingreverse-transcription-based PCR assays (RT-PCR), e.g., using primersspecific for the differentially expressed sequences. RNA can also bequantified using, for example, other target amplification methods (e.g.,TMA, SDA, NASBA), or signal amplification methods (e.g., bDNA), and thelike.

Alternatively, ALLDBRISK protein and nucleic acid metabolites can bemeasured. The term “metabolite” includes any chemical or biochemicalproduct of a metabolic process, such as any compound produced by theprocessing, cleavage or consumption of a biological molecule (e.g., aprotein, nucleic acid, carbohydrate, or lipid). Metabolites can bedetected in a variety of ways known to one of skill in the art,including the refractive index spectroscopy (RI), ultra-violetspectroscopy (UV), fluorescence analysis, radiochemical analysis,near-infrared spectroscopy (near-IR), nuclear magnetic resonancespectroscopy (NMR), light scattering analysis (LS), mass spectrometry,pyrolysis mass spectrometry, nephelometry, dispersive Ramanspectroscopy, gas chromatography combined with mass spectrometry, liquidchromatography combined with mass spectrometry, matrix-assisted laserdesorption ionization-time of flight (MALDI-TOF) combined with massspectrometry, ion spray spectroscopy combined with mass spectrometry,capillary electrophoresis, NMR and IR detection. (See, WO 04/056456 andWO 04/088309, each of which are hereby incorporated by reference intheir entireties.) In this regard, other ALLDBRISK analytes can bemeasured using the above-mentioned detection methods, or other methodsknown to the skilled artisan. For example, circulating calcium ions(Ca²⁺) can be detected in a sample using fluorescent dyes such as theFluo series, Fura-2A, Rhod-2, among others. Other ALLDBRISK metabolitescan be similarly detected using reagents that are specifically designedor tailored to detect such metabolites.

Kits

Kits for practicing the methods of the invention are provided. The kitsinclude (a) one or more reagents for measuring the amount of one or morelipid metabolites (and/or additional biomarkers); and (b) instructionsfor use. A kit may provide 1, 2, 3, 4, 5, 10, 15, 20, or more reagentsfor measuring the amount of 1, 2, 3, 4, 5, 10, 15, 20, or more lipidmetabolites. The kit may further provide one or more reagents formeasuring one or more additional biomarkers, such as those disclosedabove, and in Tables X-X. In one embodiment, the kit includes one ormore reagents for use in an immunoassay. In one embodiment, the kitincludes one or more reagents for use in an MS assay. The invention isfurther illustrated by the following nonlimiting examples.

The invention also includes a ALLDBRISK-detection reagent, e.g., nucleicacids that specifically identify one or more ALLDBRISK nucleic acids byhaving homologous nucleic acid sequences, such as oligonucleotidesequences or aptamers, complementary to a portion of the ALLDBRISKnucleic acids or antibodies to proteins encoded by the ALLDBRISK nucleicacids packaged together in the form of a kit. The oligonucleotides canbe fragments of the ALLDBRISK genes. For example the oligonucleotidescan be 200, 150, 100, 50, 25, 10 or less nucleotides in length. The kitmay contain in separate containers a nucleic acid or antibody (eitheralready bound to a solid matrix or packaged separately with reagents forbinding them to the matrix), control formulations (positive and/ornegative), and/or a detectable label such as fluorescein, greenfluorescent protein, rhodamine, cyanine dyes, Alexa dyes, luciferase,radiolabels, among others. Instructions (e.g., written, tape, VCR,CD-ROM, etc.) for carrying out the assay may be included in the kit. Theassay may for example be in the form of a Northern hybridization or asandwich ELISA as known in the art.

For example, ALLDBRISK detection reagents can be immobilized on a solidmatrix such as a porous strip to form at least one ALLDBRISK detectionsite. The measurement or detection region of the porous strip mayinclude a plurality of sites containing a nucleic acid. A test strip mayalso contain sites for negative and/or positive controls. Alternatively,control sites can be located on a separate strip from the test strip.Optionally, the different detection sites may contain different amountsof immobilized nucleic acids, e.g., a higher amount in the firstdetection site and lesser amounts in subsequent sites. Upon the additionof test sample, the number of sites displaying a detectable signalprovides a quantitative indication of the amount of ALLDBRISKS presentin the sample. The detection sites may be configured in any suitablydetectable shape and are typically in the shape of a bar or dot spanningthe width of a test strip.

Alternatively, the kit contains a nucleic acid substrate arraycomprising one or more nucleic acid sequences. The nucleic acids on thearray specifically identify one or more nucleic acid sequencesrepresented by ALLDBRISKS 1-271. In various embodiments, the expressionof 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 40, 50, 100, 125, 150, 175,200, 210, 220, 230, 240, 250, 260 or more of the sequences representedby ALLDBRISKS 1-271 can be identified by virtue of binding to the array.The substrate array can be on, e.g., a solid substrate, e.g., a “chip”as described in U.S. Pat. No. 5,744,305. Alternatively, the substratearray can be a solution array, e.g., xMAP (Luminex, Austin, Tex.),Cyvera (Alumina, San Diego, Calif.), CellCard (Vitra Bioscience,Mountain View, Calif.) and Quantum Dot's Mosaic (Invitrogen, Carlsbad,Calif.).

Suitable sources for antibodies for the detection of ALLDBRISK includecommercially available sources such as, for example, Abazyme, Abnova,Affinity Biologicals, AntibodyShop, Biogenesis, Biosense Laboratories,Calbiochem, Cell Sciences, Chemicon International, Chemokine, Clontech,Cytolab, DAKO, Diagnostic BioSystems, eBioscience, EndocrineTechnologies, Enzo Biochem, Eurogentec, Fusion Antibodies, GenesisBiotech, GloboZymes, Haematologic Technologies, Immunodetect,Immunodiagnostik, Immunometrics, Immunostar, Immunovision, BioGenex,Invitrogen, Jackson ImmunoResearch Laboratory, KMI Diagnostics, KomaBiotech, LabFrontier Life Science Institute, Lee Laboratories,Lifescreen, Maine Biotechnology Services, Mediclone, MicroPharm Ltd.,ModiQuest, Molecular Innovations, Molecular Probes, Neoclone, Neuromics,New England BioLabs, Novocastra, Novus Biologicals, Oncogene ResearchProducts, Orbigen, Oxford Biotechnology, Panvera, PerkinElmer LifeSciences, Pharmingen, Phoenix Pharmaceuticals, Pierce Chemical Company,Polymun Scientific, Polysciences, Inc., Promega Corporation,Proteogenix, Protos Immunoresearch, QED Biosciences, Inc., R&D Systems,Repligen, Research Diagnostics, Roboscreen, Santa Cruz Biotechnology,Seikagaku America, Serological Corporation, Serotec, SigmaAldrich,StemCell Technologies, Synaptic Systems GmbH, Technopharm, Terra NovaBiotechnology, TiterMax, Trillium Diagnostics, Upstate Biotechnology, USBiological, Vector Laboratories, Wako Pure Chemical Industries, andZeptometrix. However, the skilled artisan can routinely make antibodies,nucleic acid probes, e.g., oligonucleotides, aptamers, siRNAs, antisenseoligonucleotides, against any of the ALLDBRISK in Table 7.

EXAMPLES Example 1

This is a description of calculating Risk using the algorithm LDA andthe formula set out as (DRS=exp(D)/[1+exp (D)]) in order to select someof the preferred protein biomarkers of Table 1.

Marker Selection

An exemplary data set collected from human subjects included 632observations in this data set and 65 potential blood-borne biomarkers(Inputs). To reduce the number of Inputs, three broad marker selectionalgorithms were used: Univariate marker selection, exhaustive smallmodel searches, and bootstrap replicates of common heuristic markerselection techniques. The bootstrap marker selection process includedforward, backward, and stepwise selection based on Akaike's informationcriteria (AIC) and Hoetelling's T², Analysis of variance based filters,random forest filters and Eigengene-based linear discriminant analysis.These selection techniques were used on 100 bootstrap replicates and themarker counts were tabulated and averaged. To control for model size,marker counts were weighted by 1/k where k is the size of the model.Markers were selected for modeling based on a permutation test asfollows: Algorithm outputs were permuted and the 100 bootstrapreplicates were used to calculate weighted marker count averages of thesix selection techniques. This process was repeated 20 times and the95th percentile of the weighted marker count averages was used as acutoff to identify markers that were selected significantly more thanrandom. Similar permutation techniques were used to identify univariatefeatures and exhaustive searches that were different from random.

Algorithm Construction

The markers selected as described above were then combined to calculatecoefficients that result in a functioning model. Logistic regressionand/or linear discriminant analysis were used to estimate coefficientsbased on maximum likelihood and least-squares means, respectively.Initially, individual markers were evaluated for linearity using decileplots and transformations were attempted if strong departures werenoted. Models including all markers were then constructed and thecoefficients were examined to determine if all were necessary. Theability to reduce the marker number is evaluated using regression modelsof principal components of the Inputs, backward selection, andbootstrapping methods. The remaining parameters were used to produce analgorithm that is a linear model constructed at a prior probability of50% group membership for the each of the two model outputs. Thisweighting is useful in balancing sensitivity and specificity of theresulting model when the number of cases and controls (also known asconverters and non-converters, respectively) are imbalanced. Cases referto the samples that were being analyzed to determine if different thanthe control.

For illustrative purposes, exemplary coefficients for selectedbiomarkers with the resulting intercept for analysis are set out inTable 10 below. The transformed values for the biomarkers are also setout under subject 20311 (1) and 77884 (0).

TABLE 10 LDA.BWD LDA.SWS LDA.KW10 LDA.RF10 LDA.ELDA3 LDA.ELDA2 20311 (1)7844 (0) Intercept −26.4567 −27.9154 −25.1138 −25.4264 −5.96578 −13.1593ADIPOQ −0.66724 −0.74205 −0.13523 −0.47984 3.837386 3.59833 CHOL−2.66393 0.90309 0.690196 CRP 0.70821 0.717325 0.603214 0.514556 0.62774.136395 2.709206 DPP4 0.078344 2.624639 2.55854 ENG −1.12999 −1.140160.433883 −0.025635 FTH1 0.711809 0.706316 0.473219 0.389999 0.6209510.586941 3.600816 3.079284 GH1 −0.23073 −0.04613 −0.331038 −0.607982GLUCOSE 17.46311 17.41075 17.37771 16.54193 19.69818 0.812913 0.653213GPT 1.087745 1.021178 0.788968 0.325215 0.441237 HBA1C 12.05816 11.239729.050276 10.31996 0.770852 0.755875 HDL 0.390531 0.269513 0.093422 HGF0.026509 −0.10911 −0.201097 −0.417961 HSPA1B 0.789939 1.238439 0.348427IGFBP1 0.045342 0.294254 0.918387 IGFBP2 −0.00518 −0.01889 20.6815414.95522 1L18 0.759557 1.049944 0.808142 0.820012 −0.702241 −0.627808IL2RA 0.60912 0.74837 −0.787264 −0.301986 INSULIN 0.665954 0.8829261.194011 1.36753 1.576526 1.103641 1.869232 0.954243 LEP 0.6965870.69285 0.658789 1.016614 0.35699 PLAT −0.99971 −0.94709 1.0247780.885599 SELE −0.51067 1.978515 2.085064 SELP −0.2501 2.539756 2.537585SERPINE1 0.019556 −0.08744 7.794406 4.859024 SGK −0.39277 3.0192463.989198 SHBG −0.39018 4.185424 3.527613 TRIG 0.846546 0.591921 0.4952680.848019 0.171855 0.079181 −0.09691 VCAM1 0.995924 1.073903 0.4979952.726349 2.497237 VEGF 0.653159 −0.53022 −1.569929 VWF 0.226829 −0.084.484484 3.835305

Calculation of Risk

The algorithm produced a linear predictor, lp, that is related to groupmembership of a sample (e.g., case or controls), assuming a 50% priorprobability of belonging to a group of converters being a case. This lpcan be converted to a convenient score for an individual subject (DRS)on a 0-10 scale using the following equation:

DRS=10*e ^(lp)/(l+e ^(lp))

This score correlates with the absolute risk of conversion at aspecified prior probability (assuming a specified probability of 50%).Changing the prior probability that was used to construct the algorithmto a probability that reflects the actual percentage of “cases” in thepopulation (based on epidemiology data of that population) effectivelyshifts the linear model by changing the intercept term, α, as follows:

α′=α+ln(π₁/π₀)

Where α′ is the new intercept, α is the intercept assuming a 50% prior,π₁ is the prior probability of being a case and π₀ is the priorprobability of being a control. The remaining coefficients stay the sameand a new linear predictor, lp′, is computed. From this Risk is computedas follows:

Risk=e ^(lp)/(1+e ^(lp′))

The Risk is the probability that a subject would become a case (aconverter). For example, a risk of 25% indicates that 25% of the peoplewith a similar DRS will convert to a diabetic within 5 years.

Example Calculation of Risk

To calculate risk for algorithm LDA.BWD in Table 10, the followingbiomarker value coefficients and intercept were used: intercept 26.4567,ADIPOQ coefficient−0.66724, CHOL coefficient−2.66393, CRP coefficient0.70821, ENG coefficient−1.12999, FTH1 coefficient 0.711809, GLUCOSEcoefficient 17.46311, GPT coefficient 1.087745, HBA1C coefficient12.05816, INSULIN coefficient 665954, LEP coefficient 0.696587, PLATcoefficient−0.99971, TRIG coefficient 0.846546, and VCAM1 coefficient0.995924.

For two subjects the transformed biomarker values (concentrationmeasured) as indicated in Table 10 the lp and score were calculated asfollows and set out in Table 11.

lp=(ADIPOQ*−0.66724)+(CHOL*−2.66393)+(CRP*0.70821)+(ENG*−1.12999)+(FTH1*0.711809)+(GLUCOSE*17.46311)+(GPT*1.087745)+(HBA1C*12.05816)+(INSULIN*665954)+(LEP*0.696587)+(PLAT*−0.99971)+(TRIG*0.846546)+(VCAM1*0.995924)+−26.4567

DRS=10*e ^(lp)/(l+e ^(lp))

TABLE 11 Subjects Group Lp DRS 77884 0 1.426083 8.062902 20311 1−2.41455 0.820701

To calculate Risk the prior predictability is shifted in view of theepidemiology data of the population that the subject being analyzed is amember. In this example the prior predictability is shifted to 12.5%,and using the following equation the resulting new intercept (α′) is−28.4026:

α′=α+ln(π₁/π₀)

Using the new intercept the adjusted linear predictor (lp′) and Risk iscalculated using the following equations. The risk scores are set out inTable 12.

lp=(ADIPOQ*−0.66724)+(CHOL*−2.66393)+(CRP*0.70821)+(ENG*−1.12999)+(FTH1*0.711809)+(GLUCOSE*17.46311)+(GPT*1.087745)+(HBA1C*12.05816)+(INSULIN*665954)+(LEP*0.696587)+(PLAT*−0.99971)+(TRIG*0.846546)+(VCAM1*0.995924)+−24.5108

Risk=e ^(lp′)/(1+e ^(lp′))

TABLE 12 Subjects Group lp′ Score Risk 77884 0 −0.51983 8.0629020.372893 20311 1 −4.36046 0.820701 0.012611

Example 2 Selection of Protein Biomarkers Using the Inter99 Cohort

This is a discussion of an exemplary method to select some of thepreferred protein biomarkers of the invention.

Study Population

The Inter99 cohort consists of 61,301 subjects aged 30-60 years from theDanish Civil Registration System. Although this was a lifestyleintervention trial for cardiovascular disease, registered atClinicalTrials.gov, Identifier NCT00289237 (14), the 5-year rate ofprogression to type 2 diabetes observed in this study (3.4%) was similarto other estimates of progression for this age group (16). A sample of13,016 was randomly selected, 12,934 were eligible and invited for anexamination, and 6784 (52.5%) attended the investigation (17). Eligibleindividuals (n=6536) were re-invited after five years and 4511 (69%)attended. Fasting blood samples, lifestyle data, blood pressure, waistcircumference, plasma lipids, and OGTT results were collected atbaseline and 5-year time points. An “at-risk” sub-population was definedas those with BMI≧25 kg/m², age ≧39 years and free of diabetes atbaseline. Among these individuals, 174 progressed to type 2 diabetesduring the 5-year follow-up (converters), and baseline samples wereavailable for 160, while 2872 did not progress (non-converters).Diagnosis of type 2 diabetes was defined by 2-hour plasma glucoseof >11.1 mmol/l in an OGTT, or >7.0 mmol/L for FPG. Non-converters(n=472) were randomly selected in an approximately 3:1 ratio toconverters.

Clinical and Standard Laboratory Measurements

Anthropometric measurements, blood pressure, routine laboratory measures(FPG, insulin, lipids), and the OGTT were performed as previouslydescribed (17). Serum was stored at −19° C.

Candidate Biomarker Selection

Potential biomarkers were identified by searching the PubMed databaseusing search terms relevant to the development of diabetes. Of 260candidate biomarkers identified as involved in pathways associated withmetabolic or cardiovascular disorders, obesity, cell death, orinflammatory response, assay reagents were obtained for 89 of thesebiomarkers. Data from 58 candidates met our quality control criteria,which required that results from ≧66% of the samples fell within theassay's linear dynamic range.

Molecular Assays

Sandwich immunoassays developed for the 58 proteins typically used amonoclonal capture antibody and a fluorescently-labeled detectionantibody. Biomarker candidates were measured using an ultrasensitivemolecular counting technology (MCT) platform (Singulex, St. Louis, Mo.).Details regarding assay reagents have been previously described (18).Briefly, detection of labeled antibodies was performed on the ZeptX™System, where liquid from each well is pumped through an interrogationspace within a capillary flow cell. Laser light (wavelength ˜650 nm) isdirected into the interrogation space, and the resulting emission fromeach labeled antibody (wavelength 668 nm) is measured via a confocalmicroscope with a photon detector.

For biomarkers in the model, reagents were obtained from R&D Systems(Minneapolis, Minn.) individually (monomeric adiponectin (ADIPOQ)) or asDuoSet Kits (interleukin 2 receptor A (IL2RA)), and United StatesBiological (Swampscott, Mass.) (C-reactive protein (CRP), ferritin heavychain 1 (FTH1)). Detection antibodies for ADIPOQ, CRP, and FTH1 wereconjugated with Alexa Fluor® 647 (Invitrogen, Carlsbad, Calif.)according to manufacturer's instructions, and purified byultrafiltration with Microcon YM-30 from Millipore (Billerica, Mass.).Analytes detected using DuoSet Kits utilized biotinylated detectionantibodies and Alexa Fluor® 647-conjugated streptavidin (Invitrogen).

One biomarker was measured per 384 microwell plate, using an average of1.3 μl serum in a total assay volume of 10 μl per well. Biomarkerconcentrations were calculated as the mean of three replicates. Assayshad dynamic ranges of 10² to 10³, intra-plate CVs of ≦5%, and an averagelower limit of detection of 10 pg/ml.

Model Development Process

A model development process was devised which applied multiplestatistical approaches in which a limited number of the most informativemarkers would be selected for inclusion. Sixty-four candidate biomarkerswere evaluated for inclusion in multi-marker models: six routinelaboratory measures (FPG, fasting serum insulin, triglycerides, totalcholesterol, HDL cholesterol, and LDL cholesterol), and 58 serumproteins. Biomarker candidates were selected for inclusion in the modelbased on frequency of selection in four statistical learning approaches.The four approaches were referred to as U (univariate logisticregression analyses), E (exhaustive enumeration of small (≦6)multivariate logistic models), H (six different heuristic model-buildingmethods, including forward, backward, and stepwise selection,Kruskal-Wallis test, random forest, and Eigengene-based lineardiscriminant analysis with three different statistical learningalgorithms, including logistic regression, linear discriminant analysis,and support vector machines), and B (frequency of selection within 100bootstrap replicates using the same basic heuristic model-buildingmethods).

Permutation testing was used to establish a threshold of selectionfrequency for inclusion of a biomarker in the model. For the permutationtesting, the entire selection procedure was repeated using a datasetwith randomly assigned outcomes. To be included in the model, abiomarker's selection frequency in the dataset with non-permuted (true)outcomes had to fall outside the 95% confidence interval of itsselection frequency using the dataset with randomly assigned outcomes.To make the model more parsimonious, the selected biomarkers weresubjected to backward selection, sequentially removing biomarkers untilall remaining biomarkers were significant at the 90% confidence level.

Results

Baseline characteristics of converter and non-converter groups aresummarized in Table 13.

Converters Non-Converters p Participants 160 472 Male 110 (68.8%) 279(59.1%) 0.031 NFG and NGT 12 (7.6%) 226 (49.7%) <0.0001 IFG only 46(29.1%) 174 (38.2%) 0.0433 IGT only 25 (15.8%) 19 (4.2%) <0.0001 BothIFG and IGT 75 (47.5%) 36 (7.9%) <0.0001 Family history 48 (30%) 98(20.8%) 0.0223 Age (yrs) 50.2 (45.2-55.0) 49.8 (44.8-54.8) <0.0001Height (cm) 172 (166-179) 172 (166-179) 0.9277 Weight (kg) 89 (80-100)84 (77-93) 0.0001 BMI (kg/m2) 29.7 (27.5-32.9) 27.6 (26.1-30.1) <0.0001Waist (cm) 97 (91-109) 93 (86-99) <0.0001 Hip (cm) 106 (102-113) 104(100-109) 0.004 Systolic blood pressure (mmHg) 140 (130-150) 130(120-144) <0.0001 Diastolic blood pressure (mmHg) 90 (80-96) 85 (80-90)0.0008 Fasting serum total cholesterol 5.8 (5.1-6.5) 5.7 (5.0-6.4)0.2513 (mmol/L) Fasting serum HDL cholesterol 1.2 (1.0-1.4) 1.3(1.1-1.6) 0.0013 (mmol/L) Fasting serum LDL cholesterol 3.6 (3.1-4.4)3.6 (3.1-4.3) 0.6898 (mmol/L) Fasting serum triglycerides 1.6 (1.3-2.2)1.3 (0.9-1.8) <0.0001 (mmol/L) Fasting serum insulin (pmol/L) 58 (37-81)40 (27-59) <0.0001 2-h serum insulin (pmol/L) 325 (210-486) 186(100-298) <0.0001 Fasting plasma glucose (mmol/L) 6.1 (5.7-6.5) 5.6(5.3-6.0) <0.0001 2-h plasma glucose (mmol/L) 8.4 (7.1-9.5) 6.1(5.1-7.0) <0.0001 HbAlc (%) 6.1 (5.8-6.4) 5.9 (5.6-6.1) <0.0001Adiponectin (μg/mL) 19.5 (9.3-39.6) 22.2 (12.9-42.6) 0.0345 CRP (μg/mL)3.2 (1.5-7.9) 2.0 (0.8-5.3) <0.0001 Ferritin (ng/mL) 867 (290-1749) 483(168-1045) <0.0001 IL2RA (pg/mL) 290 (230-400) 270 (200-350) 0.0049

Applying the model development process to all 64 candidate biomarkers(58 serum proteins and six routine laboratory measures), CRP, FTH1,glucose, alanine aminotransferase, and insulin were selected by all fourapproaches (U, E, H, B), insulin-like growth factor binding protein 2(IGFBP2), IL2RA and heat shock 70 kDa protein 1B (HSPA1B) were selectedby three approaches (E, H, B), leptin and interleukin 18 (IL18) wereselected by two approaches (U, E), and ADIPOQ was selected by oneapproach (E). After backward selection, the resulting DRS model includedsix biomarkers (ADIPOQ, CRP, FTH1, glucose, IL2RA, and insulin). Theperformance of this model was estimated using the bootstrap re-samplingapproach. FIG. 1 compares the area under the ROC curves for the fittedperformance of this DRS model to assess 5-year type 2 diabetes risk inthe dataset (AUC=0.78) to that of this DRS model using bootstrapre-sampling of the dataset (AUC=0.76). The similarity of the AUCssuggests that this model is not over-fit and is likely to be robust whenused to assess risk in a different population. The similarity inperformance between the bootstrap estimate of performance on thetraining set and performance on a sequestered validation datasetvalidates using the bootstrap approach to estimate model performance.

FIG. 2 compares the AUC of this DRS model to that of several routinelaboratory measures (HbA1c, FPG, fasting serum insulin, 2-hour seruminsulin and 2-hour plasma glucose from the OGTT), two clinical variables(BMI and gender-adjusted waist), a model using fasting glucose andinsulin, and a non-invasive clinical model (age, BMI, waistcircumference, and family history of type 2 diabetes in first-degreerelatives). The AUC of this DRS model is statistically significantlydifferent from that of single marker measures from fasting bloodsamples, a model using fasting glucose and insulin, anthropometricmeasures, and a clinical index, while it is equivalent to 2-hour glucose(from OGTT) and 2-hour insulin (p=0.18, p=0.70, respectively). Addingfamily history, age, BMI and waist circumference components of thenon-invasive model to this DRS model improved the fit slightly(p=0.0067, likelihood ratio test), but produced only a marginalperformance gain (AUC 0.792 vs. 0.780, p=0.059). It should also be notedthat the DRS average for females is 1.35 lower than males (p<0.0001).However, this gender difference accurately reflects the difference inrisk of developing diabetes and the performance of the DRS is equivalentin both sexes (AUC=0.770 and 0.783 for females and males, respectively;p=0.7908).

In order to extrapolate results from this nested case-control study tothe entire at-risk population within the Inter99 cohort, and to providea way to convert a DRS to the absolute risk of developing diabetes foran individual, Bayes' Law was applied to adjust for the observed 5.7%5-year rate of conversion to diabetes for the population with BMI≧25kg/m² and age ≧39 years.

FIG. 3 compares the stratification of risk achieved by measuring FPG and2-hour glucose to that achieved using this DRS model. FIG. 3A shows thatthe DRS provides a continuous measure of risk of progressing to type 2diabetes in the at-risk population. FIG. 3B illustrates the risk levelby FPG class, using the threshold of 100 mg/dL for IFG. The IFG grouphas a 5-year conversion risk which is 1.4-fold higher than the pre-testprobability, and comprises 56% of the at-risk population. FIG. 3Cillustrates the level of risk in each stratum when using this DRS tostratify the individuals into low-, medium-, and high-risk groups.Individuals in the high-risk group have a 3.5-fold increased risk overthe pre-test probability and comprise 10% of the population. Individualsin the low-risk group have a 3.5-fold lower risk and comprise 54% of thepopulation, and the remaining medium-risk group has a 1.3-fold increasedrisk and comprises 36% of the population. As might be expected from theAUC comparison, the risk of developing diabetes in subjects with IGT(which is 14.6% of the population) is 24.5%, which is similar to therisk in the high-risk DRS group. Yet, the low-risk group identified byDRS has a 1.6% risk of developing diabetes, which is lower than subjectswith either NFG (2.4%) or NGT (2.5%) in this study.

FIG. 3 Analysis of Biomarkers and Lipid Metabolites

In order to identify this panel of biomarkers, over 90 proteinbiomarkers in two European cohorts (Botnia and Inter99) (Lyssenko etal., Diabetes 54:166-174, 2005: Jorgensen et al, Eur. J. CardiovascPrev. Rehabil, 10:377-386, 2000) using a molecular counting technologyhighly sensitive for measuring proteins in serum. The algorithmsdeveloped from these two studies produced receiver operator curves (ROC)with area under the curve (AUC) of approximately 0.75 in a populationage ≧39 and BMI≧25.

With respect to performance assessment, two orthogonal sets of criteriawere used in marker selection. The first criteria picked markers basedon outcome studies of Diabetic Converters in 2 separate populationcohorts. A stepwise procedure of marker prioritization involved afiltering of markers based on 1) consistency of univariate performancebetween cohorts; 2) dimensionality reduction via shrinkage and step-wiseselection techniques, and 3) relative rankings using leave-one-out (LOO)and backward elimination procedures. The results of this analysis wereused to prioritize clinical, protein and lipid markers according to theability of resulting models to discriminate among outcomes (as assessedby bootstrap AUC (bsAUC)) and the relative strength of markercontribution in a multivariate algorithm as assessed by measures ofmodel fit, such as the likelihood ratio test (LRT).

A second set of criteria additionally assessed lipid markers based ontheir univariate performance in a meta-analysis of severaltreatment-based studies. Lipid markers were selected based on theconsistency of their univariate performance across multiple studies ofdrug intervention and lifestyle modifications that relate to diabetestreatment.

Marker lists from both sets of selection criteria were combined andresubmitted for dimensionality reduction using a shrinkage technique andreprioritized based on their relative performance in LOO and BWDelimination procedures as measured by the LRT using a singleoutcome-based study. This “performance-based” list of markers were theninterrogated using an Exhaustive Search technique, in which allcombinations of markers were used to build multiple models—each of whichwere comparatively assessed on a single outcome-based study usingcross-validated AUCs. The relative importance of markers were assessedby p-values of the coefficients within these models and the frequency ofselection of markers in multiple iterations of resampling.

The subset of lipid markers in the “performance-based” list was thenvetted based on considerations of analytical stability, limitations ofmeasurement, and the ability to develop assays within prescribedtimelines. Some lipids not passing this vetting procedure, were replacedwith alternative biological analogues and derivatives that also showedgood univariate performance, good signal-to-noise characteristics anddid not have limitations related to distinguishing between mass-isomers.

The resulting list of protein, clinical measures, and vetted lipidmetabolites were again subjected to an Exhaustive Search technique. Theresulting relative performance of example models as well as the relativemarker performance is noted in the attached supplementary materials. Theperformance of an example model relative to OGTT is also included.

Table 14 shows 14 markers identified to be highly predictive andincludes several different classes of molecules associated with diabetesdevelopment including growth factors, cytokines, hormones, lipids fromthe cholesterol ester and lysophosphatidylcholine classes, and specificmolecules of glucose metabolism.

TABLE 14 Informative Biomarkers in Algorithms Predicting Risk ofDiabetes within 5 years Adiponectin C-reactive protein Ferritin Glucosehemoglobin Alc (HbA1c) Insulin interleukin-2 receptor, alpha (IL2RA)Cholesteryl palmitoleate (CE 16:1n7) Cholesteryl homo gamma linoleate(CE 20:3n6) Cholesteryl linoleate (CE 18:2n6) Cholesteryl palmitate (CE16:0) Cholesteryl petroselinate (CE 18:1n9)1-Linoleoyl-2-hydroxy-sn-glycero-3-phosphocholine (LY 18:2n6)1-01eoyl-2-hydroxy-sn-glycero-3-phosphocholine (LY18:1n7 and LY18:1n9)

Results of additional testing were generated using validated assays inthe CLIA-certified Tethys Clinical Laboratory in subjects aged between30 and 60 years. The AUC in this study was 0.84 (see FIG. 4).Preliminary results incorporating specific lipid molecules into amulti-marker model showed a significant increase in the AUC.

These biomarkers span many pathways associated with progression todiabetes such as insulin resistance, obesity, endocrine disorders, andhyperglycemia. FIG. 5 shows the number of markers in each pathway thatwere shown to be informative for the prediction of diabetes in one ormore algorithms developed from the Botnia and Inter99 studies. Somemarkers overlap the various pathways.

Example 2 Validated Lipid Assays

Biological Samples were prepared via a simple internal standard spikeand protein precipitation as follows. The internal standard mix (3 oddchain analogs: CE 15:1, CE 17:0, LY 17:0) was added to 20 μl serumsample. The sample was then vortexed for at least 3 seconds. Toprecipitate the proteins, 1 mL of 50/50 MeOH/CHC13 was added to thesample followed by vortexing for at least 3 seconds. The samples werethen centrifuged for 10 minutes at 17000×G. Subsequently, thesupernatant was transferred to a 2 mL glass vial and loaded into theautosampler of the Waters ACQUITY UPLC system. The autosampler wasmaintained at 10° C. The samples were injected (2 mL per sample) onto aWaters UPLC BEH C18 column (2.1×30 mm, 1.7 mm) with a Waters VangaurdBEH C18 precolumn (2.1×5 mm, 1.7 um). The column compartment was heatedto 80° C. Solvent A was 5 mM NH4HCO2 in water. Solvent B was 5 mMNH4HCO2 in MeOH. From 0-1 min, the column was maintained at 80% solventB, then ramped to 100% B in 1 minute and held at 100% B for 1.7 minutes.The column was immediately returned to 80% and allowed to re-equilibratefor 1.2 minutes. The flow rate was 1 mL/min. The Waters ACQUITY UPLC wascoupled to a Waters Xevo TQD mass spectrometer. The analytes wereionized via positive electrospray and the mass spectrometer was operatedin tandem MS mode with argon as the collision gas. Waters MassLynxsoftware was used to automatically integrate all analyte and internalstandard chromatographic peaks. Peak area ratios (analyte/internalstandard) were then calculated and then converted to absolute analyteconcentrations using external calibration curves.

What is claimed is:
 1. A method of evaluating risk for developing adiabetic condition, the method comprising: (a) obtaining biomarkermeasurement data for an individual, wherein the biomarker measurementdata is representative of measurements of biomarkers in at least onebiological sample from the individual; wherein said biomarkers comprise:(i) glucose, (ii) at least three protein biomarkers selected from theprotein biomarkers in Table 1 and (iii) at least one lipid metaboliteselected from the lipid metabolites in Table 2; and (b) evaluating riskfor the individual developing a diabetic condition based on an outputfrom a model, wherein the model is executed based on an input of thebiomarker measurement data.
 2. The method of claim 1, wherein theobtaining step comprises measuring the biomarkers in the at least onebiological sample.
 3. The method of claim 2, further comprising a step,prior to the measuring the biomarkers, of obtaining at least onebiological sample from the individual.
 4. The method of claim 1, whereinobtaining biomarker measurement data comprises obtaining datarepresentative of a measurement of the level of at least one biomarkerfrom a preexisting record.
 5. The method of any one of claims 1 to 4,wherein the evaluating step includes comparing the biomarker measurementdata from the individual with biomarker measurement data of the samebiomarkers from a population, and evaluating risk for the individualdeveloping a diabetic condition from the comparison.
 6. The method ofany one of claims 1 to 5, further comprising displaying the riskevaluation from (b) on a visual display.
 7. The method of any one ofclaims 1 to 6, further comprising printing or storing the riskevaluation on paper or an electronic storage medium.
 8. The method ofany one of claims 1 to 7, further comprising advising said individual ora health care practitioner of said risk evaluation.
 9. The method of anyone of claims 1 to 8, further comprising: obtaining clinical measurementdata for the individual for at least one clinical parameter selectedfrom the group consisting of age, body mass index (BMI), diastolic bloodpressure (DBP), family history (FHX), past gestational diabetes mellitus(GDM), height (HT), hip circumference (Hip), race, sex, systolic bloodpressure (SBP), waist circumference (Waist), and weight (WT), whereinthe model is executed based on an input of the biomarker measurementdata and the clinical measurement data.
 10. A method of evaluating riskfor developing a diabetic condition, the method comprising: (a)obtaining measurements of biomarkers from at least one biological sampleisolated from an individual, wherein said biomarkers comprise: (i)glucose, (ii) at least three protein biomarkers selected from theprotein biomarkers in Table 1 and (iii) at least one lipid metaboliteselected from the lipid metabolites in Table 2; and (b) calculating arisk for developing a diabetic condition from the output of a model,wherein the inputs to said model comprise said measurements ofbiomarkers, and wherein said model was developed by fitting data from alongitudinal study of a population of individuals and said fitted datacomprises levels of said biomarkers and conversion to Diabetes in saidselected population of individuals.
 11. The method of claim 10, whereinthe obtaining step comprises measuring the biomarkers in the at leastone biological sample.
 12. The method of claim 10 or 11, furthercomprising displaying the calculated risk from (b) on a visual display.13. The method of any one of claims 10 to 12, further comprisingprinting or storing the calculated risk on paper or an electronicstorage medium.
 14. The method of any one of claims 10 to 13, furthercomprising advising said individual or a health care practitioner ofsaid risk evaluation.
 15. The method of any one of claims 10 to 14,further comprising: obtaining at least one clinical measurement for theindividual for at least one clinical parameter selected from the groupconsisting of age, body mass index (BMI), diastolic blood pressure(DBP), family history (FHX), past gestational diabetes mellitus (GDM),height (HT), hip circumference (Hip), race, sex, systolic blood pressure(SBP), waist circumference (Waist), and weight (WT), wherein the inputsto the model further comprise said at least one clinical measurement.16. The method of any one of claims 1 to 15, wherein the individual hasnot been previously diagnosed as having Diabetes, pre-Diabetes, or apre-diabetic condition.
 17. The method of any one of claims 1 to 15,wherein the individual has a pre-diabetic condition, and the methodevaluates or calculates risk for the individual developing Diabetes. 18.The method of any one of claims 1 to 17, wherein the individual ispregnant.
 19. The method according to any one of claims 1 to 18, whereinthe diabetic condition is selected from the group consisting of Type 2Diabetes, pre-Diabetes, Metabolic Syndrome, Impaired Glucose Tolerance,and Impaired Fasting Glycemia.
 20. The method according to any one ofclaims 1 to 19, wherein said at least one biological sample compriseswhole blood, serum, or plasma.
 21. The method according to any one ofclaims 1 to 20, wherein at least one of said biomarker measurements isobtained by a method selected from the group consisting of immunoassayand enzymatic activity assay.
 22. The method according to any one ofclaims 1 to 21, wherein the method using said biomarkers has an areaunder the ROC curve, reflecting the degree of diagnostic accuracy forpredicting development of the diabetic condition, of at least 0.75,0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, or 0.85.
 23. Themethod according to any one of claims 1 to 22, wherein the method usingsaid biomarkers has an area under the ROC curve, reflecting the degreeof diagnostic accuracy for predicting development of the diabeticcondition, of at least 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09,0.10, 0.11, 0.12, 0.13, 0.14, or 0.15 greater than a correspondingmethod wherein the biomarkers consist of the glucose and the proteinbiomarkers but not the lipid metabolites.
 24. A kit comprising reagentsfor measuring a group of biomarkers, wherein the biomarkers are: (i)glucose, (ii) at least three protein biomarkers selected from theprotein biomarkers in Table 1 and (iii) at least one lipid metaboliteselected from the lipid metabolites in Table
 2. 25. The kit of claim 24,wherein at least one of the reagents comprises a detectable label. 26.The kit of claim 24, wherein the reagents for the protein biomarkers andlipid metabolites are attached to a solid support.
 27. A computerreadable medium having computer executable instructions for evaluatingrisk for developing a diabetic condition, the computer readable mediumcomprising: a routine, stored on the computer readable medium andadapted to be executed by a processor, to store biomarker measurementdata representing measurements of at least the following: (i) glucose,(ii) at least three protein biomarkers selected from the proteinbiomarkers in Table 1 and (iii) at least one lipid metabolite selectedfrom the lipid metabolites in Table 2; and a routine stored on thecomputer readable medium and adapted to be executed by a processor toanalyze the biomarker measurement data to evaluate a risk for developinga diabetic condition.
 28. A medical diagnostic test system forevaluating risk for developing a diabetic condition, the systemcomprising: a data collection tool adapted to collect biomarkermeasurement data representative of measurements of biomarkers in atleast one biological sample from an individual, wherein said biomarkerscomprise: (i) glucose, (ii) at least three protein biomarkers selectedfrom the protein biomarkers in Table 1 and (iii) at least one lipidmetabolite selected from the lipid metabolites in Table 2; and ananalysis tool comprising a statistical analysis engine adapted togenerate a representation of a correlation between a risk for developinga diabetic condition and measurements of the biomarkers, wherein therepresentation of the correlation is adapted to be executed to generatea result; and an index computation tool adapted to analyze the result todetermine the individual's risk for developing a diabetic condition andrepresent the result as an index value.
 29. The medical diagnostic testsystem of claim 28, wherein the analysis tool comprises a first analysistool comprising a first statistical analysis engine, the system furthercomprising a second analysis tool comprising a second statisticalanalysis engine adapted to select the representation of the correlationbetween the risk for developing a diabetic condition and measurements ofthe biomarkers from among a plurality of representations capable ofrepresenting the correlation.
 30. The system of claim 28 or 29, furthercomprising a reporting tool adapted to generate a report comprising theindex value.
 31. A method of developing a model for evaluation of riskfor developing a diabetic condition, the method comprising: obtainingbiomarker measurement data, wherein the biomarker measurement data isrepresentative of measurements of biomarkers from a population andincludes endpoints of the population; wherein said biomarkers for whichmeasurement data is obtained comprise: (i) glucose, (ii) at least threeprotein biomarkers selected from the protein biomarkers in Table 1 and(iii) at least one lipid metabolite selected from the lipid metabolitesin Table 2; inputting the biomarker measurement data of at least asubset of the population into a model; and training the model forendpoints using the inputted biomarker measurement data to derive arepresentation of a correlation between a risk of developing a diabeticcondition and measurements of biomarkers in at least one biologicalsample from an individual.
 32. A method of evaluating the current statusof a diabetic condition in an individual, the method comprising:obtaining biomarker measurement data, wherein the biomarker measurementdata is representative of measurements of biomarkers in at least onebiological sample from the individual, wherein said biomarkers comprise:(i) glucose, (ii) at least three protein biomarkers selected from theprotein biomarkers in Table 1 and (iii) at least one lipid metaboliteselected from the lipid metabolites in Table 2; and evaluating thecurrent status of a diabetic condition in the individual based on anoutput from a model, wherein the model is executed based on an input ofthe biomarker measurement data.
 33. A method of evaluating a diabeticdisease surrogate endpoint an individual, the method comprising:obtaining biomarker measurement data, wherein the biomarker measurementdata is representative of measurements of biomarkers in at least onebiological sample from the individual; wherein said biomarkers comprise:(i) glucose, (ii) at least three protein biomarkers selected from theprotein biomarkers in Table 1 and (iii) at least one lipid metaboliteselected from the lipid metabolites in Table 2; and evaluating adiabetic disease surrogate endpoint in the individual based on an outputfrom a model, wherein the model is executed based on an input of thebiomarker measurement data.
 34. The method, kit, computer readablemedium, or system of any one of claims 1 to 33, wherein said biomarkerscomprise at least four, at least five, at least six, at least seven, atleast eight, at least nine, or at least ten protein biomarkers fromTable
 1. 35. The method, kit, computer readable medium, or system of anyone of claims 1 to 33, wherein said at least three protein biomarkersare selected from the group consisting of adiponectin, C-reactiveprotein (CRP), HbAlc, IGFBPI, IGFBP2, Insulin, IL2RA, ferritin, and LEP.36. The method, kit, computer readable medium, or system of any one ofclaims 1 to 33, wherein said at least three protein biomarkers areselected from the group consisting of: adiponectin, C-reactive protein(CRP), IL2RA, ferritin, insulin, and HbAlc.
 37. The method, kit,computer readable medium, or system of any one of claims 1 to 33,wherein said at least three protein biomarkers include at least oneglycemic index marker selected from insulin and HbAlc.
 38. The method,kit, computer readable medium, or system of any one of claims 1 to 33,wherein said at least three protein biomarkers comprise adiponectin,insulin, and C-reactive protein.
 39. The method, kit, computer readablemedium, or system of any one of claims 1 to 33, wherein said at leastthree protein biomarkers adiponectin, CRP and HbAlc
 40. The method, kit,computer readable medium, or system of any one of claims 1 to 33,wherein said at least three protein biomarkers are selected from thecombinations of any one of FIGS. 8-26.
 41. The method, kit, computerreadable medium, or system of any one of claims 1 to 40, wherein said atleast three protein markers and at least one lipid metabolite areselected from the combinations of any one of FIGS. 27-35.
 42. Themethod, kit, computer readable medium, or any system of any one ofclaims 1-41, wherein said biomarkers comprise at least two, at leastthree, at least four, at least five, at least six, at least seven, atleast eight, at least nine, or at least ten lipid metabolites from Table2.
 43. The method, kit, computer readable medium, or system of any oneof claims 1 to 42, wherein said at least one lipid metabolite comprisesat least one cholesterol ester.
 44. The method, kit, computer readablemedium, or system of any one of claims 1 to 42, wherein said at leastone lipid metabolite comprises at least one lipid metabolite selectedfrom the group consisting of AC6:0, AC8:0, AC10:0, CE16:0, CE16:ln7,CE18:0, CE18:3n6, CE18:ln9, CE 18:2n6, CE20:3n6, CE20:4n3, TGTL, DG16:0,DG18:0, DG18:ln9, DG18:2n6, DG18:3n3, DGTL, FA16:0, FA16:ln7, FA18:ln9,FA18:2n6, FA24:0, LY16:ln7, LY18:ln7, LY18:ln9, LY18:2n6, PC16:ln7,PC18:2n6, PC18:3n6, PC18:ln7, PC20:3n9, PC22:4n6, PC22:5n3, PCdml8:0,PCdml8:ln9, PCdml6:0, PC20:3n6, PC20:4n3, PEdm18:ln9, PE16:ln7,PE18:2n6, PE20:2n6, PE22:0, PE24:ln9 PEdml8:0, TG16:0, TG16:ln7, TG18:0,TG18:ln7, TG18:ln9, TG18:2n6 and TG18:3n3.
 45. The method, kit, computerreadable medium, or system of any one of claims 1 to 42, wherein said atleast one lipid metabolite selected from the group consisting ofCE16:ln7, CE20:3n6, CE18:2n6, CE16:0, CE18:ln9, LY18:2n6, LY18:ln7 andLY18:ln9.
 46. The method, kit, computer readable medium, or system ofany one of claims 1 to 42, wherein said at least one lipid metabolitecomprises CE 16:ln7.
 47. The method, kit, computer readable medium, orsystem of any one of claims 1 to 42, wherein said at least one lipidmetabolite comprises CE 20:3n6.
 48. The method, kit, computer readablemedium, or system of any one of claims 1 to 42, wherein said at leastone lipid metabolite comprises CE18:2n6.
 49. The method, kit, computerreadable medium, or system of any one of claims 1 to 42, wherein said atleast one lipid metabolite comprises CE16:0.
 50. The method, kit,computer readable medium, or system of any one of claims 1 to 42,wherein said at least one lipid metabolite comprises CE18:ln9.
 51. Themethod, kit, computer readable medium, or system of any one of claims 1to 42, wherein said at least one lipid metabolite comprises LY18:2n6.52. The method, kit, computer readable medium, or system of any one ofclaims 1 to 42, wherein said at least one lipid metabolite comprisesLY18:ln7 or LY18:ln9.
 53. A method of prophylaxis for Diabetescomprising: obtaining risk score data representing a Diabetes risk scorefor an individual, wherein the Diabetes risk score is computed accordingto the method of claim 2 for calculating a risk of developing a diabeticcondition; and generating prescription treatment data representing aprescription for a treatment regimen to delay or prevent the onset ofDiabetes to an individual identified by the Diabetes risk score as beingat elevated risk for Diabetes.
 54. A method of prophylaxis for Diabetescomprising: evaluating or calculating risk, for at least one subject, ofdeveloping a diabetic condition according to the method of any one ofclaims 1-23 and 34-52; and treating a subject identified as being atelevated risk for a diabetic condition with a treatment regimen to delayor prevent the onset of Diabetes.
 55. The method according to claim 53or 54, wherein the treatment regimen comprises at least one therapeuticselected from the group consisting of: INS, INS analogs, hypoglycemicagents, anti-inflammatory agents, lipid-reducing agents, calcium channelblockers, beta-adrenergic receptor blocking agents, cyclooxygenase-2(COX-2) inhibitors, prodrugs of COX-2 inhibitors, angiotensin IIantagonists, angiotensin converting enzyme (ACE) inhibitors, renininhibitors, lipase inhibitors, amylin analogs, sodium-glucosecotransporter 2 inhibitors, dual adipose triglyceride lipase and PI3kinase activators, antagonists of neuropeptide Y receptors, humanhormone analogs, cannabinoid receptor antagonists, triple monoamineoxidase reuptake inhibitors, inhibitors of norepinephrine and dopaminereuptake, inhibitors of 11 Beta-hydroxysteroid dehydrogenase type 1 (Ilb-HSDI), inhibitors of Cortisol synthesis, inhibitors ofgluconeogenesis, glucokinase activators, antisense inhibitors of proteintyrosine phosphatase-IB, islet neogenesis therapy, and betahistine. 56.The method according to claim 53 or 54, wherein the treatment regioncomprises at least one therapeutic at least one therapeutic selectedfrom the group consisting of acarbose, metformin, troglitazone, androsightazone.
 57. A method of ranking or grouping a population ofindividuals, comprising: calculating for developing a diabetic conditionaccording to the method of any one of claims 10 to 23 for individualscomprised within the population; and ranking individuals within thepopulation relative to the remaining individuals in the population ordividing the population into at least two groups, based on factorscomprising said risk for developing a diabetic condition.
 58. The methodof claim 57, further comprising using ranking data representing theranking or grouping of the population of individuals for one or more ofthe following purposes: to determine an individual's eligibility forhealth insurance; to determine an individual's premium for healthinsurance; to determine an individual's premium for membership in ahealth care plan, health maintenance organization, or preferred providerorganization; and to assign health care practitioners to an individualin a health care plan, health maintenance organization, or preferredprovider organization.
 59. The method of claim 57 or 58, furthercomprising using ranking data representing the ranking or grouping ofthe population of individuals for one or more purposes selected from thegroup consisting of: to recommend therapeutic intervention or lifestyleintervention to an individual or group of individuals; to manage thehealth care of an individual or group of individuals; to monitor thehealth of an individual or group of individuals; and to monitor thehealth care treatment, therapeutic intervention, or lifestyleintervention for an individual or group of individuals.
 60. A method ofevaluating the current status of a diabetic condition in an individual,the method comprising: obtaining biomarker measurement data, wherein thebiomarker measurement data is representative of measurements ofbiomarkers in at least one biological sample from the individual; andevaluating the current status of a diabetic condition in the individualbased on an output from a model, wherein the model is executed based onan input of the biomarker measurement data; wherein said biomarkerscomprise: (i) glucose, (ii) at least three protein biomarkers selectedfrom the protein biomarkers in Table 1 and (iii) at least one lipidmetabolite selected from the lipid metabolites in Table 2.